Add OARMP routing engine, dashboard and documentation

- Complete routing engine: ingest, optimizer (CG+B&B), maintenance
  monitor, metrics, pipeline, quality checks
- Streamlit dashboard with Input/Output tab structure, editable data
  editors, interactive Folium map with satellite layer and maintenance
  base highlights, FH stacked bar chart with TTM availability
- CSV data files: AERONAVES, CHECKS, AIRPORTS, ESCALA DE VOO
- README, CONTEXTO and CHANGELOG added
- Remove legacy pre_process scripts and raw binary files (PDFs/xlsx)
- Update .gitignore to exclude outputs/, data/, raw/*.pdf, raw/*.xlsx

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

.gitignore

@@ -1,3 +1,16 @@
+# Python
+__pycache__/
+*.py[cod]
+*.pyo
+
+# Virtual environment
+.venv/
+venv/
+env/
+
+# Logs
+logs/
+
 # Windows
 Thumbs.db
 Desktop.ini
@@ -9,3 +22,12 @@ Desktop.ini
 # Local editor folders
 .vscode/
 .idea/
+
+# Generated outputs and processed data
+outputs/
+data/
+
+# Large binary / proprietary files
+raw/*.pdf
+raw/*.xlsx
+raw/*.xls

CHANGELOG.md

@@ -0,0 +1,103 @@
+# Changelog
+
+Todas as alterações relevantes do projeto são registradas neste arquivo.  
+Formato baseado em [Keep a Changelog](https://keepachangelog.com/pt-BR/1.0.0/).
+
+---
+
+## [Não lançado]
+
+### Adicionado
+- `README.md` com documentação do projeto, estrutura de pastas, instruções de instalação e conceitos-chave
+- `CONTEXTO.md` com contexto operacional, motivação e decisões de projeto
+- `CHANGELOG.md` (este arquivo)
+
+---
+
+## [0.5.0] — 2026-06-17
+
+### Adicionado
+- **Mapa interativo de rotas** (aba `🗺 Mapa`): rotas por aeronave exibidas com `folium` + `streamlit-folium`; controle de camadas para alternar entre **CartoDB positron** e **satélite Esri World Imagery**
+- **Destaque de bases de manutenção no mapa**: marcador laranja (raio duplo) com tooltip `🔧 Base de manutenção` nos aeroportos com `IS_MAINTENANCE_BASE = 1`
+- `folium>=0.14.0` e `streamlit-folium>=0.22.0` adicionados ao `requirements.txt`
+
+### Corrigido
+- **TTM disponível zerava após check**: a lógica anterior usava apenas o primeiro evento de manutenção por aeronave e subtraía todo `fh_apos` do TTM do segundo ciclo. Agora todos os eventos são acumulados (`all_maint`) e `fh_after_last = total_fh − Σ(accum_fh_at_check)` calcula corretamente as FH voadas após a **última** manutenção
+- Linha "Média" na tabela Frota não tinha mais cor de fundo (removido `background-color:#e8f4f8`); mantém apenas negrito
+
+---
+
+## [0.4.0] — 2026-06-16
+
+### Adicionado
+- **Aba `🗺 Mapa`** no Output: representação geográfica das rotas por aeronave usando `plotly.graph_objects.Scattergeo` (substituído na v0.5.0)
+- Biblioteca `airportsdata` para lookup de coordenadas por código ICAO, sem necessidade de API key
+- `airportsdata>=20240101` adicionado ao `requirements.txt`
+- Coordenadas de DEP/ARR lidas diretamente da `Escala com Atribuição` (campo compartilhado `raw_sched_atrib`)
+
+### Alterado
+- Gráfico de FH por aeronave e métricas de horas disponíveis transferidos da aba `Escala com Atribuição` para a aba `Manutenção e Aeronaves` (renomeada)
+- Cor da coluna "FH de TTM perdidas por aeronave / ciclo" alterada para vermelho
+
+---
+
+## [0.3.0] — 2026-06-15
+
+### Adicionado
+- **Gráfico de FH por aeronave** na aba `Manutenção e Aeronaves` com quatro segmentos empilhados:
+  - FH Iniciais (cinza `#94a3b8`)
+  - FH Planejadas antes da manutenção (azul `#3b82f6`)
+  - FH Planejadas após a manutenção (âmbar `#f59e0b`)
+  - FH Disponíveis até a próxima manutenção (verde `#22c55e`, opacidade 0.55)
+  - Marcador diamante vermelho indicando o evento de check
+- **Métricas de horas disponíveis** por aeronave (metric cards) abaixo do gráfico
+- Campo para **adicionar N linhas de uma vez** na Escala de Voo (Input), com botão "Limpar tudo"
+- Exclusão de linhas habilitada na tabela de Escala de Voo (`num_rows="dynamic"`)
+
+### Alterado
+- Abas `Resumo` e `Gantt` unificadas em `📊 Resumo`
+- Altura do gráfico de FH dobrada (800 px)
+
+### Corrigido
+- Variável `_c4` renomeada para `_` (sem uso)
+- `tat` acessado via `st.session_state.get("tat", 60)` para evitar erro quando o botão de execução é pressionado antes de o sub-tab Aeronaves ser visitado
+
+---
+
+## [0.2.0] — 2026-06-14
+
+### Adicionado
+- **Aba `🔧 Manutenção`**: tabela com eventos de manutenção (aeronave, OFRAG anterior, threshold, FH acumuladas, perda de TTM, início/fim calendário) e gráficos de FH perdidas e FH verificadas
+- **Aba `✈ Frota`**: tabela de utilização com coluna `Média` e gráficos de utilização TTM e FH por aeronave
+- Campo **Tempo do Solver (s)** e botão **Executar Otimização** movidos para a aba Input (acima das sub-abas)
+- Mensagem de conclusão com **tempo decorrido** da otimização (`⏱ Otimização concluída em Xm Ys.`)
+- Cor de fundo das células da Escala com Atribuição alterada para transparente
+
+### Removido
+- Aba `Parâmetros` do sidebar (TAT mínimo movido para sub-aba Aeronaves; ano de planejamento removido — inferido de `pd.Timestamp.now().year`)
+
+### Alterado
+- Campo **TAT mínimo** movido para a sub-aba `Aeronaves` do Input
+- Largura do campo Tempo do Solver reduzida com `st.columns([1, 1, 4])`
+
+---
+
+## [0.1.0] — 2026-06-13
+
+### Adicionado
+- **Reescrita completa do dashboard** (`app/dashboard.py`) com estrutura em duas abas principais: `📥 Input` e `📤 Output`
+- Sub-abas de Input: `Escala de Voo`, `Aeronaves`, `Checks`, `Bases de Manutenção` com `st.data_editor` editável
+- Suporte a **DataFrames em memória** no pipeline: `load_from_dfs()` em `ingest.py` aceita dados do editor sem gravar em disco
+- Parâmetro `raw_dfs` em `RoutingPipeline.run()` para passar dados in-memory
+- Parsing de datas no formato `DD/MM/AAAA` em `_parse_br_date()` (além do legado `DD/mon`)
+- Backup do dashboard original em `app/dashboard_backup.py`
+
+### Alterado
+- `src/routing_engine/pipeline.py`: `run()` recebe `raw_dfs` opcional; chama `load_from_dfs` se fornecido
+- `src/routing_engine/ingest.py`: adicionada função `load_from_dfs()` com normalização de sinônimos de colunas
+
+---
+
+## [0.0.x] — antes de 2026-06-13
+
+Commits iniciais do repositório: estrutura de pastas, configuração de CI, scripts de inspeção de arquivos, engine de otimização (config, ingest, network_generator, optimizer, maintenance_monitor, metrics, pipeline, quality).

CONTEXTO.md

@@ -1,38 +1,64 @@
-# Contexto do Projeto
+# Contexto do Projeto — OARMP
 
-## Objetivo
+## Origem e motivação
 
-Organizar, preservar e processar documentos relacionados ao projeto `fab_oamrp`, mantendo rastreabilidade das alterações e separando arquivos originais, intermediários e processados.
+O OARMP nasceu da necessidade de apoiar o planejamento de emprego da frota de aeronaves C-105 Amazonas no âmbito do CEAO 809. A gestão manual da escala de voo — realizada em planilhas — não garante o respeito sistemático aos intervalos de manutenção (TTM), o que pode resultar em aeronaves com check vencido, indisponibilidade não planejada ou perda desnecessária de horas de TTM.
 
-## Estrutura de Pastas
+O sistema automatiza a atribuição de aeronaves a missões, respeitando os checks programados e minimizando o desperdício de horas de vida útil de manutenção.
 
-- `raw/`: arquivos originais, sem alteração.
-- `pre_process/`: scripts e saídas intermediárias de pré-processamento.
-- `processed/`: dados finais limpos, consolidados ou prontos para análise.
+## Contexto operacional
 
-## Colaboradores
+### Aeronaves
 
-- `VTO`: Vitor Cesa.
-- `GNR`: Generoso.
-- `JOM`: João Marcos.
+A frota considerada é composta por aeronaves **C-105 Amazonas** (Embraer KC-390 regional), operadas no transporte logístico e de pessoal na região amazônica. Cada aeronave possui um histórico acumulado de horas de voo (FH Totais) que determina a proximidade ao próximo check.
 
-## Orientações de Trabalho
+### Checks de manutenção
 
-- Manter documentos originais sempre em `raw/`.
-- Não alterar arquivos dentro de `raw/`; quando necessário, gerar cópias ou saídas em `pre_process/`.
-- Registrar no `LOG.md` toda entrada, alteração, processamento ou decisão relevante.
-- Usar o formato de log com data, hora, tag do colaborador e descrição objetiva da ação.
-- Registrar a origem de cada documento, quando conhecida.
-- Informar scripts executados e parâmetros importantes.
-- Registrar quantidade de registros extraídos, descartados, corrigidos ou validados.
-- Registrar problemas encontrados nos arquivos originais, como erros de digitação, páginas ilegíveis, campos ausentes ou tabelas quebradas.
-- Registrar decisões de padronização, por exemplo nomes de colunas, unidades, formatos de data e tratamento de acentos.
-- Explicitar os critérios usados para mover dados de `pre_process/` para `processed/`.
+Os checks são definidos por **thresholds cumulativos de FH** (ex: 300 h, 400 h, 600 h, 900 h). Quando uma aeronave atinge o threshold de seu ciclo atual, ela deve ser submetida ao check correspondente antes de continuar voando. A manutenção é realizada exclusivamente nas **bases habilitadas** (campo `IS_MAINTENANCE_BASE = 1` em AIRPORTS.csv).
 
-## Formato Recomendado do Log
+### OFRAGs
 
-```text
-| Data | Hora | Autor | Ação | Arquivos | Observações |
-| --- | --- | --- | --- | --- | --- |
-| 2026-06-15 | 15:41 | VTO | Pré-processou PDF de inspeções | raw/documento.pdf; pre_process/saida.csv | 18 inspeções extraídas |
-```
+Um OFRAG (fragmento de voo) é um conjunto de etapas que **parte da base de manutenção e retorna a ela**. Essa restrição é fundamental: apenas OFRAGs com início e fim na base podem ser alocados no problema de otimização, pois garantem que a aeronave pode ser submetida a check entre missões.
+
+### Escala de voo
+
+A escala é fornecida no formato da planilha operacional da unidade, com etapas identificadas por data, aeroportos de partida/chegada, horários e número de OFRAG. O sistema aceita datas no formato `DD/MM/AAAA` ou no formato legado `DD/mon` (ex: `15/jan`).
+
+## Problema de otimização
+
+O problema é modelado como **Set Partitioning** sobre o conjunto de OFRAGs:
+
+- Cada OFRAG deve ser coberto por exatamente uma aeronave
+- Cada aeronave executa no máximo uma rota (sequência de OFRAGs)
+- A rota deve ser TTM-viável: a qualquer ponto, as FH acumuladas desde o último check não excedem o TTM do ciclo atual
+- A função objetivo minimiza a **perda total de TTM** (horas de TTM não utilizadas quando a manutenção é antecipada)
+
+A solução é obtida por **Column Generation** (geração de colunas) combinada com **Branch & Bound** (PuLP/CBC), permitindo explorar um espaço de rotas potencialmente exponencial de forma eficiente.
+
+## Decisões de projeto
+
+| Decisão | Justificativa |
+|---------|---------------|
+| Set Partitioning (ao invés de Set Covering) | Garante que cada missão seja atribuída a exatamente uma aeronave, sem ambiguidade |
+| Column Generation | O número de rotas viáveis é exponencial; CG constrói apenas colunas lucrativas |
+| Pricing por DP (label-setting) | Subproblema é um shortest-path com restrições de recursos (TTM), adequado para DP em DAG |
+| TAT mínimo (padrão 60 min) | Garante tempo mínimo de preparação entre OFRAGs consecutivos na mesma aeronave |
+| Penalidade Big-M por OFRAG descoberto | Permite solução mesmo quando a cobertura total é inviável (ex: mais OFRAGs do que aeronaves disponíveis) |
+
+## Limitações conhecidas
+
+- O modelo considera apenas um evento de manutenção por ciclo de check; múltiplos checks consecutivos são tratados como ciclos independentes
+- A duração da manutenção é fixa por tipo de check; variações logísticas (falta de peças, disponibilidade de hangar) não são modeladas
+- OFRAGs que não partem e chegam à base de manutenção são excluídos do problema (não podem ser alocados)
+- O solver CBC tem desempenho limitado para instâncias muito grandes; o parâmetro `mip_time_limit_seconds` controla o tempo máximo
+
+## Arquivos de referência
+
+| Arquivo | Conteúdo |
+|---------|----------|
+| `raw/AERONAVES.csv` | Frota com FH acumuladas |
+| `raw/CHECKS.csv` | Thresholds e durações dos checks |
+| `raw/AIRPORTS.csv` | Aeroportos e bases de manutenção |
+| `raw/ESCALA DE VOO MODELO 1.csv` | Escala de missões |
+| `raw/ICA 66-31 2023.pdf` | Instrução regulatória de aeronavegabilidade (referência) |
+| `raw/Airline Operations and Scheduling*.pdf` | Referência bibliográfica principal (Bazargan, 2010) |

LOG.md

@@ -1,14 +0,0 @@
-# Log do Projeto
-
-Legenda de autores:
-
-- `VTO`: Vitor Cesa.
-- `GNR`: Generoso.
-- `JOM`: João Marcos.
-
-| Data | Hora | Autor | Ação | Arquivos | Observações |
-| --- | --- | --- | --- | --- | --- |
-| 2026-06-15 | 15:41 | VTO | Criou repositório Git do projeto | `.gitignore`; `.gitattributes` | Projeto versionado na branch `main`. |
-| 2026-06-15 | 15:41 | VTO | Organizou estrutura inicial de dados | `raw/`; `pre_process/`; `processed/` | Documentos originais movidos para `raw/`; pastas vazias preservadas com `.gitkeep`. |
-| 2026-06-15 | 15:41 | VTO | Pré-processou PDF de inspeções | `raw/documento joaomarcos.pdf`; `pre_process/documento_joaomarcos_texto.txt`; `pre_process/documento_joaomarcos_inspecoes.json`; `pre_process/documento_joaomarcos_inspecoes.csv`; `pre_process/preprocess_pdf.py` | 18 inspeções extraídas; grafias originais preservadas, inclusive `INPEÇÃO`. |
-| 2026-06-15 | 15:41 | VTO | Separou orientações permanentes do histórico | `CONTEXTO.md`; `LOG.md` | Orientações movidas para `CONTEXTO.md`; `LOG.md` passou a usar data, hora e tag de autor. |

README.md

@@ -0,0 +1,145 @@
+# OARMP — Aircraft Routing & Maintenance Planning
+
+Sistema de otimização de roteamento de aeronaves com planejamento integrado de manutenção, desenvolvido para o Curso de Especialização em Administração de Organizações (CEAO 809).
+
+## Visão geral
+
+O OARMP resolve o problema de atribuição de aeronaves a fragmentos de voo (OFRAGs) respeitando as restrições de **Time to Maintenance (TTM)** de cada aeronave, minimizando as horas perdidas de TTM ao longo do planejamento.
+
+### Formulação matemática
+
+**Set Partitioning com Column Generation + Branch & Bound**
+
+```
+Minimizar   Σ_r  c_r · x_r                          (perda total de TTM)
+s.a.        Σ_{r: j∈r}  x_r  = 1   ∀ j ∈ OFRAGs   (cada OFRAG coberto exatamente uma vez)
+            Σ_{r: a(r)=a} x_r ≤ 1  ∀ a ∈ Aeronaves (uma rota por aeronave)
+            x_r ∈ {0, 1}
+```
+
+O subproblema de precificação (pricing) é resolvido por **programação dinâmica (label-setting DP)** sobre o DAG de OFRAGs ordenados por horário de partida.
+
+## Estrutura do projeto
+
+```
+arara_oarmp/
+├── app/
+│   └── dashboard.py          # Interface Streamlit (entrada de dados + resultados)
+├── raw/                      # Dados de entrada (CSV)
+│   ├── AERONAVES.csv
+│   ├── CHECKS.csv
+│   ├── AIRPORTS.csv
+│   └── ESCALA DE VOO MODELO 1.csv
+├── src/
+│   └── routing_engine/
+│       ├── config.py         # Parâmetros e caminhos
+│       ├── ingest.py         # Leitura e normalização dos dados
+│       ├── inspect_files.py  # Detecção automática de colunas (sinônimos)
+│       ├── network_generator.py  # Grafo de adjacência entre OFRAGs
+│       ├── optimizer.py      # CG + B&B (PuLP/CBC)
+│       ├── maintenance_monitor.py  # Validação TTM e eventos de manutenção
+│       ├── metrics.py        # Tabelas e sumários de saída
+│       ├── pipeline.py       # Orquestração end-to-end
+│       └── quality.py        # Verificações de qualidade dos dados
+├── scripts/                  # Scripts auxiliares de inspeção e execução
+├── outputs/                  # Resultados gerados pelo pipeline
+│   ├── schedules/
+│   └── exports/
+├── requirements.txt
+└── run_pipeline.bat
+```
+
+## Instalação
+
+```bash
+# Criar ambiente virtual
+python -m venv .venv
+.venv\Scripts\activate        # Windows
+# source .venv/bin/activate   # Linux/Mac
+
+# Instalar dependências
+pip install -r requirements.txt
+```
+
+### Dependências principais
+
+| Pacote | Uso |
+|--------|-----|
+| `pulp` | Formulação e resolução do MIP (solver CBC) |
+| `pandas` / `numpy` | Manipulação de dados |
+| `streamlit` | Dashboard interativo |
+| `plotly` | Gráficos (Gantt, barras) |
+| `folium` + `streamlit-folium` | Mapa interativo de rotas |
+| `airportsdata` | Coordenadas geográficas de aeroportos por ICAO |
+| `networkx` | Grafo de adjacência entre OFRAGs |
+
+## Execução
+
+### Via dashboard (recomendado)
+
+```bash
+streamlit run app/dashboard.py
+```
+
+### Via script de linha de comando
+
+```bash
+python scripts/03_run_optimization_pipeline.py
+# ou
+run_pipeline.bat
+```
+
+## Dados de entrada
+
+Todos os arquivos ficam em `raw/` com separador `;`:
+
+### AERONAVES.csv
+| Campo | Descrição |
+|-------|-----------|
+| MATRICULA | Indicativo da aeronave |
+| MODELO | Modelo (ex: C105) |
+| FH TOTAIS | Horas de voo totais acumuladas |
+
+### CHECKS.csv
+| Campo | Descrição |
+|-------|-----------|
+| CHECKS | Nome do check |
+| FH | Threshold cumulativo de FH para o check |
+| TEMPO DE EXECUCAO (DIAS) | Duração do check em dias |
+| LOCAL DE EXECUCAO | Código ICAO da base de manutenção |
+
+### AIRPORTS.csv
+| Campo | Descrição |
+|-------|-----------|
+| AIRPORT_CODE | Código ICAO |
+| AIRPORT_NAME | Nome do aeroporto |
+| IS_MAINTENANCE_BASE | 1 = base de manutenção |
+
+### ESCALA DE VOO MODELO 1.csv
+Escala de missões com colunas: `DATA`, `ETAPA`, `DEP`, `ARR`, `HORA_DEP`, `HORA_ARR`, `TEMPO_VOO`, `SEGMTO`, `MISSAO`, `OFRAG`.  
+Formato de data aceito: `DD/MM/AAAA` ou `DD/mon` (ex: `15/jan`).
+
+## Dashboard — abas
+
+### Entrada (📥 Input)
+- **Escala de Voo**: tabela editável com adição/remoção de linhas
+- **Aeronaves**: frota com FH totais e TAT mínimo
+- **Checks**: thresholds e durações de manutenção
+- **Bases de Manutenção**: aeroportos habilitados para check
+
+### Saída (📤 Output)
+- **Resumo**: métricas gerais + diagrama de Gantt
+- **Escala com Atribuição**: escala original com aeronave atribuída por OFRAG
+- **Manutenção e Aeronaves**: eventos de manutenção, gráfico de FH por aeronave (FH iniciais + planejadas antes/após manutenção + TTM disponível)
+- **Frota**: utilização percentual do TTM por aeronave
+- **Mapa**: rotas por aeronave em mapa interativo (satélite Esri / CartoDB)
+
+## Conceitos-chave
+
+**OFRAG** — Fragmento de voo: conjunto de etapas que sai e retorna à base de manutenção. Unidade de alocação do problema.
+
+**TTM (Time to Maintenance)** — Horas de voo disponíveis até o próximo check obrigatório. Cada ciclo de check tem seu próprio TTM.
+
+**TTM Loss** — Horas de TTM não utilizadas quando a manutenção é realizada antes de esgotar o ciclo (inevitável quando o próximo OFRAG excederia o limite).
+
+**TAT (Turnaround Time)** — Tempo mínimo entre o pouso de um OFRAG e a decolagem do seguinte na mesma aeronave.

app/dashboard.py

@@ -0,0 +1,755 @@
+"""
+Aircraft Routing Dashboard  –  Streamlit application (v2).
+
+Run with:  streamlit run app/dashboard.py  (from project root)
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import streamlit as st
+
+from src.routing_engine import DEFAULT_CONFIG, RoutingPipeline
+
+# ── Page config ───────────────────────────────────────────────────────────────
+st.set_page_config(
+    page_title="OARMP – Aircraft Routing",
+    page_icon="✈",
+    layout="wide",
+)
+st.title("✈ Aircraft Routing & Maintenance Planning (OARMP)")
+st.caption("Set Partitioning + Column Generation + Branch & Bound")
+
+# ── Constants ─────────────────────────────────────────────────────────────────
+_RAW = Path(__file__).resolve().parents[1] / "raw"
+_SCHED_COLS = ["DATA", "ETAPA", "DEP", "ARR", "HORA_DEP", "HORA_ARR", "TEMPO_VOO", "SEGMTO", "MISSAO", "OFRAG"]
+
+
+def _read_csv(filename: str, skiprows: int = 0, names=None) -> pd.DataFrame:
+    path = _RAW / filename
+    if not path.exists():
+        return pd.DataFrame(columns=names or [])
+    for enc in ("utf-8-sig", "utf-8", "latin-1", "cp1252"):
+        try:
+            return pd.read_csv(path, sep=";", encoding=enc, skiprows=skiprows, names=names, dtype=str)
+        except Exception:
+            continue
+    return pd.DataFrame(columns=names or [])
+
+
+# ── Session-state defaults ────────────────────────────────────────────────────
+def _init_state():
+    if "df_aircraft" not in st.session_state:
+        st.session_state["df_aircraft"] = _read_csv("AERONAVES.csv")
+
+    if "df_checks" not in st.session_state:
+        st.session_state["df_checks"] = _read_csv("CHECKS.csv")
+
+    if "df_airports" not in st.session_state:
+        st.session_state["df_airports"] = _read_csv("AIRPORTS.csv")
+
+    if "df_schedule" not in st.session_state:
+        _BR_MON = {"jan":"01","fev":"02","mar":"03","abr":"04","mai":"05","jun":"06",
+                   "jul":"07","ago":"08","set":"09","out":"10","nov":"11","dez":"12"}
+        _DEFAULT_YEAR = 2026
+
+        def _to_dmy(val: str) -> str:
+            parts = str(val).strip().split("/")
+            if len(parts) == 3:
+                return val  # already DD/MM/AAAA
+            if len(parts) == 2:
+                mon = _BR_MON.get(parts[1].lower())
+                if mon:
+                    return f"{int(parts[0]):02d}/{mon}/{_DEFAULT_YEAR}"
+            return val
+
+        df = _read_csv("ESCALA DE VOO MODELO 1.csv", skiprows=2, names=_SCHED_COLS)
+        df = df[df["DATA"].notna() & (df["DATA"].str.strip() != "")]
+        df = df[df["OFRAG"].notna() & (df["OFRAG"].str.strip() != "")]
+        df["DATA"] = df["DATA"].apply(_to_dmy)
+        st.session_state["df_schedule"] = df.reset_index(drop=True)
+
+    if "result" not in st.session_state:
+        st.session_state["result"] = None
+
+
+_init_state()
+
+# ── Main tabs ─────────────────────────────────────────────────────────────────
+tab_input, tab_output = st.tabs(["📥 Input", "📤 Output"])
+
+# ══════════════════════════════════════════════════════════════════════════════
+# INPUT TAB
+# ══════════════════════════════════════════════════════════════════════════════
+with tab_input:
+    _col_solver, _col_btn, _ = st.columns([1, 1, 4])
+    with _col_solver:
+        time_limit = st.number_input(
+            "Tempo solver (s)",
+            min_value=30, max_value=600, value=st.session_state.get("time_limit", 120),
+            step=10, key="time_limit",
+        )
+    with _col_btn:
+        st.write("")  # alinhamento vertical
+        run_btn = st.button("▶ Executar Otimização", type="primary", use_container_width=True)
+
+    st.divider()
+
+    sub_sched, sub_aircraft, sub_checks, sub_bases = st.tabs(
+        ["📋 Escala de Voo", "✈ Aeronaves", "🔧 Checks", "🏭 Bases de Manutenção"]
+    )
+
+    with sub_aircraft:
+        st.subheader("Aeronaves")
+        st.caption("Matrícula, modelo e horas de voo totais acumuladas de cada aeronave.")
+        tat = st.number_input(
+            "TAT mínimo (min)",
+            min_value=0, max_value=240,
+            value=st.session_state.get("tat", 60),
+            step=10,
+            key="tat",
+        )
+        edited = st.data_editor(
+            st.session_state["df_aircraft"],
+            num_rows="dynamic",
+            use_container_width=True,
+            key="editor_aircraft",
+            column_config={
+                "MATRICULA": st.column_config.TextColumn("Matrícula"),
+                "MODELO": st.column_config.TextColumn("Modelo"),
+                "FH TOTAIS": st.column_config.NumberColumn("FH Totais", min_value=0, format="%.0f"),
+            },
+        )
+        st.session_state["df_aircraft"] = edited
+
+    with sub_checks:
+        st.subheader("Checks de Manutenção")
+        st.caption("Tipos de check, limiar de FH, duração em dias e local de execução.")
+        edited = st.data_editor(
+            st.session_state["df_checks"],
+            num_rows="dynamic",
+            use_container_width=True,
+            key="editor_checks",
+            column_config={
+                "CHECKS": st.column_config.TextColumn("Denominação do Check"),
+                "FH": st.column_config.NumberColumn("FH Limite", min_value=0, format="%.0f"),
+                "TEMPO DE EXECUCAO (DIAS)": st.column_config.NumberColumn("Duração (dias)", min_value=0),
+                "LOCAL DE EXECUCAO": st.column_config.TextColumn("Local (ICAO)"),
+            },
+        )
+        st.session_state["df_checks"] = edited
+
+    with sub_bases:
+        st.subheader("Aeroportos e Bases de Manutenção")
+        st.caption(
+            "Lista de aeroportos utilizados. Defina IS_MAINTENANCE_BASE = 1 "
+            "para os aeroportos que são base de manutenção."
+        )
+        edited = st.data_editor(
+            st.session_state["df_airports"],
+            num_rows="dynamic",
+            use_container_width=True,
+            key="editor_airports",
+            column_config={
+                "AIRPORT_CODE": st.column_config.TextColumn("Código ICAO"),
+                "AIRPORT_NAME": st.column_config.TextColumn("Nome"),
+                "LATITUDE": st.column_config.NumberColumn("Latitude", format="%.4f"),
+                "LONGITUDE": st.column_config.NumberColumn("Longitude", format="%.4f"),
+                "IS_MAINTENANCE_BASE": st.column_config.SelectboxColumn(
+                    "Base de Manutenção?", options=["0", "1"]
+                ),
+                "COUNTRY": st.column_config.TextColumn("País"),
+            },
+        )
+        st.session_state["df_airports"] = edited
+
+    with sub_sched:
+        st.subheader("Escala de Voo")
+        st.caption(
+            "Etapas agrupadas em OFRAGs. "
+            "Cada OFRAG deve iniciar e terminar na base de manutenção para ser elegível."
+        )
+
+        edited = st.data_editor(
+            st.session_state["df_schedule"],
+            num_rows="dynamic",
+            use_container_width=True,
+            key="editor_schedule",
+            column_config={
+                "DATA": st.column_config.TextColumn("Data (DD/MM/AAAA)"),
+                "ETAPA": st.column_config.NumberColumn("Etapa", min_value=1),
+                "DEP": st.column_config.TextColumn("Partida (ICAO)"),
+                "ARR": st.column_config.TextColumn("Destino (ICAO)"),
+                "HORA_DEP": st.column_config.TextColumn("Hora Dep (Z)"),
+                "HORA_ARR": st.column_config.TextColumn("Hora Arr (Z)"),
+                "TEMPO_VOO": st.column_config.TextColumn("Tempo Voo"),
+                "SEGMTO": st.column_config.NumberColumn("Segmento"),
+                "MISSAO": st.column_config.TextColumn("Missão"),
+                "OFRAG": st.column_config.NumberColumn("OFRAG", min_value=1),
+            },
+        )
+        st.session_state["df_schedule"] = edited
+
+        # Controles de adição em lote e limpeza
+        _c1, _c2, _c3, _ = st.columns([1, 1, 1, 3])
+        with _c1:
+            n_add = st.number_input(
+                "Qtd. de linhas", min_value=1, max_value=100, value=1, step=1,
+                key="n_add_sched",
+            )
+        with _c2:
+            st.write("")
+            if st.button("➕ Adicionar", key="btn_add_sched", use_container_width=True):
+                empty = pd.DataFrame([{col: None for col in _SCHED_COLS}] * int(n_add))
+                st.session_state["df_schedule"] = pd.concat(
+                    [st.session_state["df_schedule"], empty], ignore_index=True
+                )
+                st.rerun()
+        with _c3:
+            st.write("")
+            if st.button("🗑 Limpar tudo", key="btn_clear_sched", use_container_width=True):
+                st.session_state["df_schedule"] = pd.DataFrame(columns=_SCHED_COLS)
+                st.rerun()
+
+# ── Run pipeline (after input tabs so session state is current) ───────────────
+if run_btn:
+    cfg = DEFAULT_CONFIG
+    cfg.tat_minutes = int(st.session_state.get("tat", 60))
+    cfg.planning_year = pd.Timestamp.now().year
+    cfg.mip_time_limit_seconds = int(time_limit)
+
+    raw_dfs = {
+        "aircraft": st.session_state["df_aircraft"],
+        "checks": st.session_state["df_checks"],
+        "airports": st.session_state["df_airports"],
+        "schedule": st.session_state["df_schedule"],
+    }
+
+    with st.spinner("Executando otimização…"):
+        try:
+            import time as _time
+            _t0 = _time.perf_counter()
+            pipe = RoutingPipeline(cfg)
+            st.session_state["result"] = pipe.run(save_outputs=True, raw_dfs=raw_dfs)
+            _elapsed = _time.perf_counter() - _t0
+            _mins, _secs = divmod(int(_elapsed), 60)
+            _dur = f"{_mins}m {_secs}s" if _mins else f"{_secs}s"
+            st.success(f"Otimização concluída em {_dur}.")
+        except Exception as exc:
+            st.error(f"Erro: {exc}")
+            st.exception(exc)
+
+result = st.session_state.get("result")
+
+# ══════════════════════════════════════════════════════════════════════════════
+# OUTPUT TAB
+# ══════════════════════════════════════════════════════════════════════════════
+with tab_output:
+    if result is None:
+        st.info("Configure o cenário na aba Input e execute a otimização.")
+    else:
+        (
+            sub_resumo,
+            sub_escala,
+            sub_maint,
+            sub_frota,
+            sub_mapa,
+        ) = st.tabs(
+            [
+                "📊 Resumo",
+                "📋 Escala com Atribuição",
+                "🔧 Manutenção e Aeronaves",
+                "✈ Frota",
+                "🗺 Mapa",
+            ]
+        )
+
+        # ── Escala com atribuição (construída uma vez, reutilizada em sub_escala e sub_mapa) ──
+        _sched_result = result["schedule"]
+        _raw = st.session_state["df_schedule"].copy()
+        _ofrag_map: dict[int, str] = {}
+        for _, _r in _sched_result.iterrows():
+            try:
+                _ofrag_map[int(_r["ofrag_id"].replace("OFRAG", ""))] = _r["aircraft"]
+            except Exception:
+                pass
+        _raw.insert(
+            0, "AERONAVE",
+            _raw["OFRAG"].apply(
+                lambda x: _ofrag_map.get(int(str(x).strip()), "—")
+                if str(x).strip().isdigit() else "—"
+            ),
+        )
+        raw_sched_atrib = _raw  # DataFrame completo com coluna AERONAVE
+
+        # ── Resumo + Gantt ────────────────────────────────────────────────────
+        with sub_resumo:
+            s = result["summary"]
+            c1, c2, c3, c4 = st.columns(4)
+            c1.metric("Status", s["status"])
+            c2.metric("FH Perdidas (objetivo)", f"{s['total_ttm_loss_hours']:.2f} h")
+            c3.metric("OFRAGs cobertas", f"{s['covered_ofrags']} / {s['total_ofrags']}")
+            c4.metric("Eventos de manutenção", s["n_maintenance_events"])
+
+            if s["uncovered_ofrags"]:
+                st.warning(f"OFRAGs NÃO cobertas: {s['uncovered_ofrags']}")
+
+            qc = result.get("quality", {})
+            if qc.get("issues"):
+                with st.expander("Avisos de qualidade de dados"):
+                    for issue in qc["issues"]:
+                        st.warning(issue)
+
+            st.caption(f"Colunas geradas no CG: {s['columns_generated']}")
+
+            st.divider()
+
+            sched = result["schedule"]
+            maint = result["maintenance"]
+            gantt_rows = []
+
+            for _, row in sched.iterrows():
+                if pd.isna(row.get("departure")) or pd.isna(row.get("arrival")):
+                    continue
+                gantt_rows.append(
+                    dict(
+                        Task=row["aircraft"],
+                        Start=row["departure"],
+                        Finish=row["arrival"],
+                        Type="OFRAG",
+                        Label=row["ofrag_id"],
+                    )
+                )
+
+            for _, row in maint.iterrows():
+                if pd.isna(row.get("maint_start")) or pd.isna(row.get("maint_end")):
+                    continue
+                gantt_rows.append(
+                    dict(
+                        Task=row["aircraft"],
+                        Start=row["maint_start"],
+                        Finish=row["maint_end"],
+                        Type="Manutenção",
+                        Label=f"CHECK {row['fh_threshold']:.0f}h (perde {row['ttm_loss_hours']:.1f}h)",
+                    )
+                )
+
+            if gantt_rows:
+                df_g = pd.DataFrame(gantt_rows)
+                fig = px.timeline(
+                    df_g,
+                    x_start="Start",
+                    x_end="Finish",
+                    y="Task",
+                    color="Type",
+                    text="Label",
+                    title="Escala de Voo e Manutenção por Aeronave",
+                    color_discrete_map={"OFRAG": "#00CC96", "Manutenção": "#EF553B"},
+                )
+                fig.update_yaxes(categoryorder="category ascending")
+                fig.update_traces(textposition="inside")
+                n_ac = df_g["Task"].nunique()
+                fig.update_layout(height=420 + n_ac * 40)
+                st.plotly_chart(fig, use_container_width=True)
+                try:
+                    fig.write_html(str(DEFAULT_CONFIG.figures_dir / "gantt.html"))
+                except Exception:
+                    pass
+            else:
+                st.info("Nenhum dado de Gantt disponível.")
+
+        # ── Escala com Atribuição ─────────────────────────────────────────────
+        with sub_escala:
+            st.subheader("Escala de Voo com Atribuição de Aeronaves")
+            if _sched_result.empty:
+                st.info("Sem escala otimizada disponível.")
+            else:
+                st.dataframe(raw_sched_atrib, use_container_width=True)
+
+        # ── Manutenção ────────────────────────────────────────────────────────
+        with sub_maint:
+            st.subheader("Eventos de Manutenção Planejados")
+            maint = result["maintenance"]
+
+            if maint.empty:
+                st.success("Nenhum evento de manutenção forçado no período planejado.")
+            else:
+                # Enrich with check name and location from input data
+                checks_input = st.session_state["df_checks"].copy()
+                # Identify relevant columns by position / name patterns
+                _fh_col = next(
+                    (c for c in checks_input.columns if "FH" in c.upper() and "TEMPO" not in c.upper()),
+                    None,
+                )
+                _loc_col = next((c for c in checks_input.columns if "LOCAL" in c.upper()), None)
+                _name_col = checks_input.columns[0] if len(checks_input.columns) > 0 else None
+
+                maint_disp = maint.copy()
+                if _fh_col:
+                    checks_input[_fh_col] = pd.to_numeric(checks_input[_fh_col], errors="coerce")
+                    if _loc_col:
+                        fh_to_loc = checks_input.dropna(subset=[_fh_col]).set_index(_fh_col)[_loc_col].to_dict()
+                        maint_disp["local_execucao"] = maint_disp["fh_threshold"].map(fh_to_loc)
+                    if _name_col:
+                        fh_to_name = checks_input.dropna(subset=[_fh_col]).set_index(_fh_col)[_name_col].to_dict()
+                        maint_disp["check_nome"] = maint_disp["fh_threshold"].map(fh_to_name)
+
+                ordered_cols = [
+                    "aircraft",
+                    "check_nome",
+                    "local_execucao",
+                    "fh_threshold",
+                    "accum_fh_at_check",
+                    "maint_start",
+                    "maint_end",
+                    "ttm_loss_hours",
+                ]
+                show_cols = [c for c in ordered_cols if c in maint_disp.columns]
+                rename_map = {
+                    "aircraft": "Aeronave",
+                    "check_nome": "Check",
+                    "local_execucao": "Local",
+                    "fh_threshold": "FH Limite",
+                    "accum_fh_at_check": "FH na Manutenção",
+                    "maint_start": "Início",
+                    "maint_end": "Término",
+                    "ttm_loss_hours": "FH Perdidas",
+                }
+                st.dataframe(
+                    maint_disp[show_cols].rename(columns=rename_map),
+                    use_container_width=True,
+                )
+
+                col_a, col_b = st.columns(2)
+                with col_a:
+                    fig_loss = px.bar(
+                        maint_disp,
+                        x="aircraft",
+                        y="ttm_loss_hours",
+                        title="FH de TTM perdidas por aeronave / ciclo",
+                        labels={"ttm_loss_hours": "TTM perdido (h)", "aircraft": "Aeronave"},
+                        text_auto=".2f",
+                        color_discrete_sequence=["#ef4444"],
+                    )
+                    st.plotly_chart(fig_loss, use_container_width=True)
+
+                with col_b:
+                    fig_fh_chk = px.bar(
+                        maint_disp,
+                        x="aircraft",
+                        y="accum_fh_at_check",
+                        color="check_cycle_index",
+                        title="FH acumuladas na manutenção por aeronave",
+                        labels={"accum_fh_at_check": "FH acumuladas", "aircraft": "Aeronave"},
+                        text_auto=".1f",
+                    )
+                    st.plotly_chart(fig_fh_chk, use_container_width=True)
+
+            # ── Gráfico FH por aeronave + horas disponíveis ───────────────────
+            st.divider()
+            st.subheader("Horas de Voo por Aeronave")
+
+            fleet_df = result["fleet"]
+            maint_df = result["maintenance"]
+            ac_input = st.session_state["df_aircraft"].copy()
+
+            _mat_col = next(
+                (c for c in ac_input.columns if any(k in c.upper() for k in ("MATRICULA", "TAIL", "AC"))),
+                ac_input.columns[0],
+            )
+            _fh_col_ac = next((c for c in ac_input.columns if "FH" in c.upper()), None)
+            fh_lookup: dict = {}
+            if _fh_col_ac:
+                ac_input["_fh"] = pd.to_numeric(ac_input[_fh_col_ac], errors="coerce").fillna(0)
+                fh_lookup = ac_input.set_index(_mat_col)["_fh"].to_dict()
+
+            checks_inp = st.session_state["df_checks"].copy()
+            _ck_fh_col = next(
+                (c for c in checks_inp.columns if "FH" in c.upper() and "TEMPO" not in c.upper()), None
+            )
+            _thresholds = sorted(
+                pd.to_numeric(checks_inp[_ck_fh_col], errors="coerce").dropna().tolist()
+            ) if _ck_fh_col else []
+
+            def _next_ttm(done_threshold: float) -> float:
+                for i, t in enumerate(_thresholds):
+                    if abs(t - done_threshold) < 0.01:
+                        if i + 1 < len(_thresholds):
+                            return _thresholds[i + 1] - t
+                        return t - (_thresholds[i - 1] if i > 0 else 0)
+                return 0.0
+
+            # all_maint: all events per aircraft sorted by time
+            all_maint: dict = {}
+            first_maint: dict = {}
+            if not maint_df.empty:
+                for ac, grp in maint_df.sort_values("maint_start").groupby("aircraft"):
+                    events = grp[["accum_fh_at_check", "fh_threshold"]].to_dict("records")
+                    all_maint[ac] = events
+                    first_maint[ac] = {
+                        "fh_antes": events[0]["accum_fh_at_check"],
+                        "threshold": events[0]["fh_threshold"],
+                    }
+
+            fleet_plot = fleet_df[["aircraft", "flight_hours_scheduled", "initial_ttm_h"]].copy()
+            fleet_plot["fh_inicial"] = fleet_plot["aircraft"].map(fh_lookup).fillna(0)
+            fleet_plot["fh_antes"] = fleet_plot.apply(
+                lambda r: first_maint[r["aircraft"]]["fh_antes"]
+                if r["aircraft"] in first_maint else r["flight_hours_scheduled"], axis=1,
+            )
+            fleet_plot["fh_apos"] = fleet_plot.apply(
+                lambda r: max(0.0, r["flight_hours_scheduled"] - first_maint[r["aircraft"]]["fh_antes"])
+                if r["aircraft"] in first_maint else 0.0, axis=1,
+            )
+            # fh_disp: remaining TTM after the LAST maintenance event
+            # fh_after_last = total FH - sum of all accum_fh_at_check (each event resets the counter)
+            fleet_plot["fh_disp"] = fleet_plot.apply(
+                lambda r: max(0.0,
+                    _next_ttm(all_maint[r["aircraft"]][-1]["fh_threshold"])
+                    - max(0.0, r["flight_hours_scheduled"]
+                          - sum(e["accum_fh_at_check"] for e in all_maint[r["aircraft"]]))
+                ) if r["aircraft"] in all_maint
+                else max(0.0, r["initial_ttm_h"] - r["flight_hours_scheduled"]),
+                axis=1,
+            )
+
+            fig_fh = go.Figure()
+            fig_fh.add_trace(go.Bar(
+                name="FH Iniciais", x=fleet_plot["aircraft"], y=fleet_plot["fh_inicial"],
+                marker_color="#94a3b8",
+                text=fleet_plot["fh_inicial"].apply(lambda v: f"{v:.0f}h"), textposition="inside",
+            ))
+            fig_fh.add_trace(go.Bar(
+                name="FH Planejadas (antes manut.)", x=fleet_plot["aircraft"], y=fleet_plot["fh_antes"],
+                marker_color="#3b82f6",
+                text=fleet_plot["fh_antes"].apply(lambda v: f"{v:.1f}h" if v > 0 else ""), textposition="inside",
+            ))
+            fig_fh.add_trace(go.Bar(
+                name="FH Planejadas (após manut.)", x=fleet_plot["aircraft"], y=fleet_plot["fh_apos"],
+                marker_color="#f59e0b",
+                text=fleet_plot["fh_apos"].apply(lambda v: f"{v:.1f}h" if v > 0 else ""), textposition="inside",
+            ))
+            fig_fh.add_trace(go.Bar(
+                name="FH Disponíveis (próx. manut.)", x=fleet_plot["aircraft"], y=fleet_plot["fh_disp"],
+                marker_color="#22c55e", opacity=0.55,
+                text=fleet_plot["fh_disp"].apply(lambda v: f"{v:.1f}h" if v > 0 else ""), textposition="inside",
+            ))
+            _maint_legend_added = False
+            for ac, info in first_maint.items():
+                y_mark = fh_lookup.get(ac, 0) + info["fh_antes"]
+                fig_fh.add_trace(go.Scatter(
+                    x=[ac], y=[y_mark], mode="markers+text",
+                    marker=dict(symbol="diamond", size=14, color="#ef4444",
+                                line=dict(color="white", width=1.5)),
+                    text=[f"CHECK {info['threshold']:.0f}h"], textposition="top center",
+                    name="Manutenção", showlegend=not _maint_legend_added, legendgroup="maint",
+                ))
+                _maint_legend_added = True
+
+            fig_fh.update_layout(
+                barmode="stack",
+                title="Horas de Voo por Aeronave — Histórico + Planejadas + Disponíveis",
+                xaxis_title="Aeronave", yaxis_title="Horas de Voo (FH)", height=800,
+                legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
+            )
+            st.plotly_chart(fig_fh, use_container_width=True)
+
+            st.subheader("Horas disponíveis até a próxima manutenção")
+            disp_cols = st.columns(len(fleet_plot))
+            for col, (_, row) in zip(disp_cols, fleet_plot.iterrows()):
+                col.metric(
+                    label=row["aircraft"],
+                    value=f"{row['fh_disp']:.1f} h",
+                    delta=f"limiar: {first_maint[row['aircraft']]['threshold']:.0f}h"
+                    if row["aircraft"] in first_maint else None,
+                )
+
+        # ── Frota ─────────────────────────────────────────────────────────────
+        with sub_frota:
+            st.subheader("Frota – Utilização das Aeronaves")
+            fleet_df = result["fleet"].copy()
+
+            # ── Métricas gerais ───────────────────────────────────────────────
+            avg_util = fleet_df["ttm_utilisation_pct"].mean()
+            total_fh = fleet_df["flight_hours_scheduled"].sum()
+            n_active = int((~fleet_df["idle"]).sum())
+
+            m1, m2, m3, m4 = st.columns(4)
+            m1.metric("Utilização Média TTM", f"{avg_util:.1f}%")
+            m2.metric("FH Totais Planejadas", f"{total_fh:.1f} h")
+            m3.metric("Aeronaves Ativas", f"{n_active} / {len(fleet_df)}")
+            m4.metric("FH Médias por Aeronave", f"{total_fh / len(fleet_df):.1f} h" if len(fleet_df) else "—")
+
+            # ── Tabela com linha "Média" ───────────────────────────────────────
+            numeric_cols = ["initial_ttm_h", "flight_hours_scheduled", "ttm_utilisation_pct",
+                            "n_ofrags_assigned", "n_maintenance_events", "total_ttm_loss_h"]
+            avg_row = {col: round(fleet_df[col].mean(), 2) for col in numeric_cols if col in fleet_df.columns}
+            avg_row["aircraft"] = "Média"
+            avg_row["model"] = "—"
+            avg_row["idle"] = False
+
+            fleet_display = pd.concat(
+                [fleet_df, pd.DataFrame([avg_row])],
+                ignore_index=True,
+            )
+
+            rename_fleet = {
+                "aircraft": "Aeronave",
+                "model": "Modelo",
+                "initial_ttm_h": "TTM Inicial (h)",
+                "flight_hours_scheduled": "FH Planejadas",
+                "ttm_utilisation_pct": "Utilização TTM (%)",
+                "n_ofrags_assigned": "OFRAGs Atribuídas",
+                "n_maintenance_events": "Eventos Manut.",
+                "total_ttm_loss_h": "FH Perdidas",
+                "idle": "Ociosa",
+            }
+
+            def _style_frota(row):
+                if row.get("Aeronave") == "Média":
+                    return ["font-weight:bold"] * len(row)
+                return [""] * len(row)
+
+            st.dataframe(
+                fleet_display.rename(columns=rename_fleet)
+                .style.apply(_style_frota, axis=1),
+                use_container_width=True,
+            )
+
+            # ── Gráfico: utilização TTM ───────────────────────────────────────
+            fig_util = px.bar(
+                fleet_df,
+                x="aircraft",
+                y="ttm_utilisation_pct",
+                title="Utilização do TTM por aeronave (%)",
+                labels={"ttm_utilisation_pct": "Utilização TTM (%)", "aircraft": "Aeronave"},
+                text_auto=".1f",
+                color="ttm_utilisation_pct",
+                color_continuous_scale="RdYlGn",
+                range_color=[0, 100],
+            )
+            fig_util.add_hline(
+                y=avg_util,
+                line_dash="dash",
+                line_color="navy",
+                annotation_text=f"Média: {avg_util:.1f}%",
+                annotation_position="top right",
+            )
+            st.plotly_chart(fig_util, use_container_width=True)
+
+            # ── Gráfico: FH detalhado ─────────────────────────────────────────
+            fig_fh = px.bar(
+                fleet_df,
+                x="aircraft",
+                y=["initial_ttm_h", "flight_hours_scheduled", "total_ttm_loss_h"],
+                barmode="group",
+                title="TTM inicial vs FH planejadas vs FH perdidas",
+                labels={"value": "Horas de voo", "aircraft": "Aeronave", "variable": ""},
+            )
+            fig_fh.update_layout(
+                legend=dict(
+                    orientation="h",
+                    yanchor="bottom",
+                    y=1.02,
+                    xanchor="right",
+                    x=1,
+                )
+            )
+            st.plotly_chart(fig_fh, use_container_width=True)
+
+        # ── Mapa ──────────────────────────────────────────────────────────────
+        with sub_mapa:
+            st.subheader("Rotas por Aeronave")
+
+            if _sched_result.empty:
+                st.info("Sem escala otimizada disponível.")
+            else:
+                import airportsdata as _apdata
+                _icao_db = _apdata.load("ICAO")
+
+                def _coord(icao: str):
+                    ap = _icao_db.get(icao.strip().upper())
+                    return {"_lat": ap["lat"], "_lon": ap["lon"]} if ap else None
+
+                # Usa exatamente os campos DEP/ARR da Escala com Atribuição
+                legs_atrib = raw_sched_atrib[raw_sched_atrib["AERONAVE"] != "—"].copy()
+
+                import folium
+                from streamlit_folium import st_folium
+
+                _palette_hex = [
+                    "#e41a1c", "#377eb8", "#4daf4a", "#984ea3",
+                    "#ff7f00", "#a65628", "#f781bf", "#999999",
+                ]
+
+                m = folium.Map(location=[-8, -60], zoom_start=4, tiles=None)
+
+                folium.TileLayer(
+                    tiles="CartoDB positron",
+                    name="Mapa base",
+                    control=True,
+                ).add_to(m)
+                folium.TileLayer(
+                    tiles="https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}",
+                    attr="Esri World Imagery",
+                    name="Satélite",
+                    control=True,
+                ).add_to(m)
+                folium.LayerControl(position="topright").add_to(m)
+
+                # Linhas de rota por aeronave
+                for i, (ac, legs) in enumerate(legs_atrib.groupby("AERONAVE")):
+                    color = _palette_hex[i % len(_palette_hex)]
+                    for _, leg in legs.sort_values(["OFRAG", "ETAPA"]).iterrows():
+                        dep = str(leg["DEP"]).strip().upper()
+                        arr = str(leg["ARR"]).strip().upper()
+                        c_dep = _coord(dep)
+                        c_arr = _coord(arr)
+                        if c_dep and c_arr:
+                            folium.PolyLine(
+                                [(c_dep["_lat"], c_dep["_lon"]), (c_arr["_lat"], c_arr["_lon"])],
+                                color=color,
+                                weight=2.5,
+                                opacity=0.85,
+                                tooltip=f"{ac}: {dep} → {arr}",
+                            ).add_to(m)
+
+                # Bases de manutenção a partir da tabela de aeroportos
+                _ap_df = st.session_state["df_airports"].copy()
+                _ap_df.columns = [c.strip().upper() for c in _ap_df.columns]
+                _base_col = next((c for c in _ap_df.columns if "MAINTENANCE" in c or "BASE" in c), None)
+                _code_col = next((c for c in _ap_df.columns if "CODE" in c or "ICAO" in c), None)
+                _maint_bases: set = set()
+                if _base_col and _code_col:
+                    _mask = _ap_df[_base_col].astype(str).str.strip().isin(["1", "True", "true"])
+                    _maint_bases = set(_ap_df.loc[_mask, _code_col].str.strip().str.upper())
+
+                # Marcadores dos aeroportos usados
+                used_codes = set(
+                    legs_atrib["DEP"].str.strip().str.upper().tolist()
+                    + legs_atrib["ARR"].str.strip().str.upper().tolist()
+                )
+                for code in used_codes:
+                    c = _coord(code)
+                    if c:
+                        is_base = code in _maint_bases
+                        folium.CircleMarker(
+                            location=[c["_lat"], c["_lon"]],
+                            radius=10 if is_base else 5,
+                            color="#f59e0b" if is_base else "#1e293b",
+                            weight=2.5 if is_base else 1.5,
+                            fill=True,
+                            fill_color="#f59e0b" if is_base else "#1e293b",
+                            fill_opacity=0.95,
+                            tooltip=f"🔧 Base de manutenção: {code}" if is_base else code,
+                            popup=f"<b>{code}</b><br>Base de manutenção" if is_base else code,
+                        ).add_to(m)
+
+                st_folium(m, height=700, use_container_width=True)

app/dashboard_backup.py

@@ -0,0 +1,232 @@
+"""
+Aircraft Routing Dashboard  –  Streamlit application.
+
+Run with:  streamlit run app/dashboard.py  (from project root)
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+# Allow imports from src/ when running from project root
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from datetime import timedelta
+
+from src.routing_engine import RoutingPipeline, DEFAULT_CONFIG
+
+# ── Page config ───────────────────────────────────────────────────────────────
+st.set_page_config(
+    page_title="OARMP – Aircraft Routing",
+    page_icon="✈",
+    layout="wide",
+)
+st.title("✈ Aircraft Routing & Maintenance Planning (OARMP)")
+st.caption("Set Partitioning + Column Generation + Branch & Bound")
+
+# ── Sidebar – parameters ──────────────────────────────────────────────────────
+with st.sidebar:
+    st.header("⚙ Parameters")
+    tat = st.number_input("TAT mínimo (min)", min_value=0, max_value=240, value=60, step=10)
+    year = st.number_input("Ano do planejamento", min_value=2024, max_value=2030, value=2026)
+    time_limit = st.number_input("Limite tempo solver (s)", min_value=30, max_value=600, value=120)
+    run_btn = st.button("▶ Executar Otimização", type="primary")
+
+# ── Session state ─────────────────────────────────────────────────────────────
+if "result" not in st.session_state:
+    st.session_state["result"] = None
+
+# ── Run pipeline ──────────────────────────────────────────────────────────────
+if run_btn:
+    cfg = DEFAULT_CONFIG
+    cfg.tat_minutes = tat
+    cfg.planning_year = int(year)
+    cfg.mip_time_limit_seconds = int(time_limit)
+
+    with st.spinner("Executando otimização…"):
+        try:
+            pipe = RoutingPipeline(cfg)
+            st.session_state["result"] = pipe.run(save_outputs=True)
+            st.success("Otimização concluída!")
+        except Exception as exc:
+            st.error(f"Erro: {exc}")
+            st.exception(exc)
+
+result = st.session_state.get("result")
+
+# ── Tabs ──────────────────────────────────────────────────────────────────────
+tab_summary, tab_gantt, tab_maint, tab_fleet, tab_ofrags = st.tabs(
+    ["📊 Resumo", "📅 Gantt", "🔧 Manutenção", "✈ Frota", "📋 OFRAGs"]
+)
+
+# ── Tab: Summary ──────────────────────────────────────────────────────────────
+with tab_summary:
+    if result is None:
+        st.info("Execute a otimização para ver os resultados.")
+    else:
+        s = result["summary"]
+        c1, c2, c3, c4 = st.columns(4)
+        c1.metric("Status", s["status"])
+        c2.metric("Objetivo (FH perdidas)", f"{s['total_ttm_loss_hours']:.2f} h")
+        c3.metric("OFRAGs cobertas", f"{s['covered_ofrags']} / {s['total_ofrags']}")
+        c4.metric("Eventos de manutenção", s["n_maintenance_events"])
+
+        if s["uncovered_ofrags"]:
+            st.warning(f"OFRAGs NÃO cobertas: {s['uncovered_ofrags']}")
+
+        st.subheader("Escala de voo otimizada")
+        sched = result["schedule"]
+        if not sched.empty:
+            st.dataframe(
+                sched.style.apply(
+                    lambda r: ["background-color:#fff3cd" if r.get("maintenance_before") else "" for _ in r],
+                    axis=1,
+                ),
+                use_container_width=True,
+            )
+
+        st.caption(f"Colunas geradas no CG: {s['columns_generated']}")
+
+# ── Tab: Gantt ────────────────────────────────────────────────────────────────
+with tab_gantt:
+    if result is None:
+        st.info("Execute a otimização para ver o diagrama de Gantt.")
+    else:
+        sched = result["schedule"]
+        maint = result["maintenance"]
+
+        gantt_rows = []
+
+        if not sched.empty:
+            for _, row in sched.iterrows():
+                if pd.isna(row.get("departure")) or pd.isna(row.get("arrival")):
+                    continue
+                gantt_rows.append(
+                    dict(
+                        Task=row["aircraft"],
+                        Start=row["departure"],
+                        Finish=row["arrival"],
+                        Resource=row["ofrag_id"],
+                        Type="OFRAG",
+                        Label=row["ofrag_id"],
+                    )
+                )
+
+        if not maint.empty:
+            for _, row in maint.iterrows():
+                if pd.isna(row.get("maint_start")) or pd.isna(row.get("maint_end")):
+                    continue
+                gantt_rows.append(
+                    dict(
+                        Task=row["aircraft"],
+                        Start=row["maint_start"],
+                        Finish=row["maint_end"],
+                        Resource="Manutenção",
+                        Type="Maintenance",
+                        Label=f"CHECK (perde {row['ttm_loss_hours']:.1f}h)",
+                    )
+                )
+
+        if gantt_rows:
+            df_gantt = pd.DataFrame(gantt_rows)
+            color_map = {"Manutenção": "#636EFA"}
+            fig = px.timeline(
+                df_gantt,
+                x_start="Start",
+                x_end="Finish",
+                y="Task",
+                color="Type",
+                text="Label",
+                title="Escala de Voo e Manutenção por Aeronave",
+                color_discrete_map={"OFRAG": "#00CC96", "Maintenance": "#EF553B"},
+            )
+            fig.update_yaxes(categoryorder="category ascending")
+            fig.update_traces(textposition="inside")
+            fig.update_layout(height=400 + len(sched["aircraft"].unique()) * 40)
+            st.plotly_chart(fig, use_container_width=True)
+
+            cfg_obj = DEFAULT_CONFIG
+            try:
+                fig.write_html(str(cfg_obj.figures_dir / "gantt.html"))
+            except Exception:
+                pass
+        else:
+            st.info("Nenhum dado de Gantt disponível.")
+
+# ── Tab: Maintenance ──────────────────────────────────────────────────────────
+with tab_maint:
+    if result is None:
+        st.info("Execute a otimização para ver eventos de manutenção.")
+    else:
+        maint = result["maintenance"]
+        if maint.empty:
+            st.success("Nenhum evento de manutenção forçado.")
+        else:
+            st.dataframe(maint, use_container_width=True)
+
+            fig_loss = px.bar(
+                maint,
+                x="aircraft",
+                y="ttm_loss_hours",
+                color="check_cycle_index",
+                title="Horas de TTM perdidas por aeronave / ciclo de check",
+                labels={"ttm_loss_hours": "TTM perdido (h)", "aircraft": "Aeronave"},
+                text_auto=".2f",
+            )
+            st.plotly_chart(fig_loss, use_container_width=True)
+
+# ── Tab: Fleet ────────────────────────────────────────────────────────────────
+with tab_fleet:
+    if result is None:
+        st.info("Execute a otimização.")
+    else:
+        fleet_df = result["fleet"]
+        st.dataframe(fleet_df, use_container_width=True)
+
+        fig_util = px.bar(
+            fleet_df,
+            x="aircraft",
+            y="ttm_utilisation_pct",
+            title="Utilização do TTM por aeronave (%)",
+            labels={"ttm_utilisation_pct": "Utilização TTM (%)", "aircraft": "Aeronave"},
+            text_auto=".1f",
+            color="ttm_utilisation_pct",
+            color_continuous_scale="RdYlGn",
+            range_color=[0, 100],
+        )
+        st.plotly_chart(fig_util, use_container_width=True)
+
+        fig_fh = px.bar(
+            fleet_df,
+            x="aircraft",
+            y=["initial_ttm_h", "flight_hours_scheduled", "total_ttm_loss_h"],
+            barmode="group",
+            title="TTM inicial vs FH planejadas vs FH perdidas",
+            labels={"value": "Horas de voo", "aircraft": "Aeronave"},
+        )
+        st.plotly_chart(fig_fh, use_container_width=True)
+
+# ── Tab: OFRAGs ───────────────────────────────────────────────────────────────
+with tab_ofrags:
+    if result is None:
+        st.info("Execute a otimização.")
+    else:
+        ofrags = result["ofrags"]
+        st.write(f"**{len(ofrags)} OFRAGs** elegíveis (iniciam e terminam na base de manutenção).")
+        cols_show = [c for c in ["ofrag_id", "departure", "arrival", "flight_hours", "n_legs", "missions", "origin", "destination"] if c in ofrags.columns]
+        st.dataframe(ofrags[cols_show], use_container_width=True)
+
+        fig_fh = px.bar(
+            ofrags,
+            x="ofrag_id",
+            y="flight_hours",
+            title="Horas de voo por OFRAG",
+            labels={"flight_hours": "FH total", "ofrag_id": "OFRAG"},
+            text_auto=".2f",
+        )
+        st.plotly_chart(fig_fh, use_container_width=True)

pre_process/.gitkeep

@@ -1 +0,0 @@
-

pre_process/documento_joaomarcos_inspecoes.csv

@@ -1,19 +0,0 @@
-seq;sigla_mnt;descricao_mnt;referencia;tipo_vencimento;zera;controle_original;intervalo_horas_voo;intervalo_meses_continuos;intervalo_pousos;intervalos
-2;INSP 1A;INSPEÇÃO CHECK 1A;Especial (a contar dela mesma - Tipo D);Término da Anterior;N;A B 3 3 D;300:00 HORAS DE VOO;8 MESES CONTÍNUOS;;300:00 HORAS DE VOO | 8 MESES CONTÍNUOS
-3;INSP 2A;INSPEÇÃO CHECK 2A;Especial (a contar dela mesma - Tipo D);Término da Anterior;N;A B 10 4 D;600:00 HORAS DE VOO;16 MESES CONTÍNUOS;;600:00 HORAS DE VOO | 16 MESES CONTÍNUOS
-4;INSP 3A;INSPEÇÃO CHECK 3A;Especial (a contar dela mesma - Tipo D);Término da Anterior;N;A B 10 5 D;900:00 HORAS DE VOO;24 MESES CONTÍNUOS;;900:00 HORAS DE VOO | 24 MESES CONTÍNUOS
-5;INSP 2Y;INSPEÇÃO CALENDÁRICA DE 2 YEARS;Especial (a contar dela mesma - Tipo D);Término da Anterior;N;B B 5 5 D;;24 MESES CONTÍNUOS;;24 MESES CONTÍNUOS
-6;CHECK 300;CHECK OPERACIONAL DE 300FH;Especial (a contar dela mesma - Tipo D);Término da Anterior;N;A O 6 H;300:00 HORAS DE VOO;;;300:00 HORAS DE VOO
-7;CHECK 400;CHECK OPERACIONAL DE 400FH;Especial (a contar dela mesma - Tipo D);Término da Anterior;N;A O 6 H;400:00 HORAS DE VOO;;;400:00 HORAS DE VOO
-8;1-INSP AOL;INSPEÇÃO AOL 295-019;Última inspeção;Término da Anterior;N;B 1 D;;;3800 POUSOS;3800 POUSOS
-9;2-INSP AOL;INSPEÇÃO AOL 295-019;Última Inspeção Específica 8 1-INSP AOL;Término da Anterior;N;B 1 D;;;2000 POUSOS;2000 POUSOS
-10;3-INSP AOL;INSPEÇÃO AOL 295-019;Última Inspeção Específica 9 2-INSP AOL;Término da Anterior;N;B 1 D;;;2000 POUSOS;2000 POUSOS
-14;INSP 8Y;INSPEÇÃO CALENDÁRICA DE 8 YEARS;Especial (a contar dela mesma - Tipo D);Término da Anterior;N;B P 15 20 D;;96 MESES CONTÍNUOS;;96 MESES CONTÍNUOS
-15;INSP 1C;INSPEÇÃO CHECK 1C;Especial (a contar dela mesma - Tipo D);Término da Anterior;N;C P 10 65 D;2400:00 HORAS DE VOO;48 MESES CONTÍNUOS;;2400:00 HORAS DE VOO | 48 MESES CONTÍNUOS
-16;INSP 2C;INSPEÇÃO GERAL CHECK 2C;Especial (a contar dela mesma - Tipo D);Término da Anterior;N;C P 60 10 D;4800:00 HORAS DE VOO;96 MESES CONTÍNUOS;;4800:00 HORAS DE VOO | 96 MESES CONTÍNUOS
-17;INSP 4Y;INSPEÇÃO CALENDÁRICA DE 4 YEARS;Especial (a contar dela mesma - Tipo D);Término da Anterior;N;B P 10 10 D;;48 MESES CONTÍNUOS;;48 MESES CONTÍNUOS
-18;INSP 100FH BR01;INSPEÇÃO DE 100 FH;Especial (a contar dela mesma - Tipo D);Término da Anterior;N;O 1 H;100:00 HORAS DE VOO;;;100:00 HORAS DE VOO
-19;INSP 2400F BR01;INPEÇÃO 2400FH;Especial (a contar dela mesma - Tipo D);Término da Anterior;N;O 3 H;2400:00 HORAS DE VOO;;;2400:00 HORAS DE VOO
-20;INSP 3000F BR01;INSPEÇÃO 3000FH;Especial (a contar dela mesma - Tipo D);Término da Anterior;N;B 1 D;3000:00 HORAS DE VOO;;;3000:00 HORAS DE VOO
-22;INSP 1000F;INPEÇÃO 1000FH;Especial (a contar dela mesma - Tipo D);Término da Anterior;N;B 1 D;1000:00 HORAS DE VOO;;;1000:00 HORAS DE VOO
-23;INSP 50FH;INSPEÇÃO 50 FH;Especial (a contar dela mesma - Tipo D);Término da Anterior;N;B 1 D;50:00 HORAS DE VOO;;;50:00 HORAS DE VOO

pre_process/documento_joaomarcos_inspecoes.json

@@ -1,309 +0,0 @@
-{
-  "fonte": "raw\\documento joaomarcos.pdf",
-  "metadados": {
-    "data_relatorio": "15/06/2026",
-    "hora_relatorio": "13:23:38",
-    "pn": "ANV C-105",
-    "cff": "0117B",
-    "nomenclatura": "AERONAVE, ASA FIXA, CASA 295 (C-105 AMAZONAS)",
-    "sn": "038",
-    "matricula": "2805",
-    "modelo": "C-105",
-    "ciclo_atual": "3650007082"
-  },
-  "inspecoes": [
-    {
-      "seq": 2,
-      "sigla_mnt": "INSP 1A",
-      "descricao_mnt": "INSPEÇÃO CHECK 1A",
-      "referencia": "Especial (a contar dela mesma - Tipo D)",
-      "tipo_vencimento": "Término da Anterior",
-      "zera": "N",
-      "controle_original": "A B 3 3 D",
-      "intervalo_horas_voo": "300:00 HORAS DE VOO",
-      "intervalo_meses_continuos": "8 MESES CONTÍNUOS",
-      "intervalo_pousos": "",
-      "intervalos": [
-        "300:00 HORAS DE VOO",
-        "8 MESES CONTÍNUOS"
-      ],
-      "linha_original": "2 INSP 1A INSPEÇÃO CHECK 1A Especial (a contar dela mesma Término da Anterior N A B 3 3 D 300:00 HORAS DE VÔO  - Tipo D) 8 MESES CONTÍNUOS"
-    },
-    {
-      "seq": 3,
-      "sigla_mnt": "INSP 2A",
-      "descricao_mnt": "INSPEÇÃO CHECK 2A",
-      "referencia": "Especial (a contar dela mesma - Tipo D)",
-      "tipo_vencimento": "Término da Anterior",
-      "zera": "N",
-      "controle_original": "A B 10 4 D",
-      "intervalo_horas_voo": "600:00 HORAS DE VOO",
-      "intervalo_meses_continuos": "16 MESES CONTÍNUOS",
-      "intervalo_pousos": "",
-      "intervalos": [
-        "600:00 HORAS DE VOO",
-        "16 MESES CONTÍNUOS"
-      ],
-      "linha_original": "3 INSP 2A INSPEÇÃO CHECK 2A Especial (a contar dela mesma Término da Anterior N A B 10 4 D 600:00 HORAS DE VÔO  - Tipo D) 16 MESES CONTÍNUOS"
-    },
-    {
-      "seq": 4,
-      "sigla_mnt": "INSP 3A",
-      "descricao_mnt": "INSPEÇÃO CHECK 3A",
-      "referencia": "Especial (a contar dela mesma - Tipo D)",
-      "tipo_vencimento": "Término da Anterior",
-      "zera": "N",
-      "controle_original": "A B 10 5 D",
-      "intervalo_horas_voo": "900:00 HORAS DE VOO",
-      "intervalo_meses_continuos": "24 MESES CONTÍNUOS",
-      "intervalo_pousos": "",
-      "intervalos": [
-        "900:00 HORAS DE VOO",
-        "24 MESES CONTÍNUOS"
-      ],
-      "linha_original": "4 INSP 3A INSPEÇÃO CHECK 3A Especial (a contar dela mesma Término da Anterior N A B 10 5 D 900:00 HORAS DE VÔO  - Tipo D) 24 MESES CONTÍNUOS"
-    },
-    {
-      "seq": 5,
-      "sigla_mnt": "INSP 2Y",
-      "descricao_mnt": "INSPEÇÃO CALENDÁRICA DE 2 YEARS",
-      "referencia": "Especial (a contar dela mesma - Tipo D)",
-      "tipo_vencimento": "Término da Anterior",
-      "zera": "N",
-      "controle_original": "B B 5 5 D",
-      "intervalo_horas_voo": "",
-      "intervalo_meses_continuos": "24 MESES CONTÍNUOS",
-      "intervalo_pousos": "",
-      "intervalos": [
-        "24 MESES CONTÍNUOS"
-      ],
-      "linha_original": "5 INSP 2Y INSPEÇÃO CALENDÁRICA DE 2 YEARS Especial (a contar dela mesma Término da Anterior N B B 5 5 D 24 MESES CONTÍNUOS  - Tipo D)"
-    },
-    {
-      "seq": 6,
-      "sigla_mnt": "CHECK 300",
-      "descricao_mnt": "CHECK OPERACIONAL DE 300FH",
-      "referencia": "Especial (a contar dela mesma - Tipo D)",
-      "tipo_vencimento": "Término da Anterior",
-      "zera": "N",
-      "controle_original": "A O 6 H",
-      "intervalo_horas_voo": "300:00 HORAS DE VOO",
-      "intervalo_meses_continuos": "",
-      "intervalo_pousos": "",
-      "intervalos": [
-        "300:00 HORAS DE VOO"
-      ],
-      "linha_original": "6 CHECK 300 CHECK OPERACIONAL DE 300FH Especial (a contar dela mesma Término da Anterior N A O 6 H 300:00 HORAS DE VÔO  - Tipo D)"
-    },
-    {
-      "seq": 7,
-      "sigla_mnt": "CHECK 400",
-      "descricao_mnt": "CHECK OPERACIONAL DE 400FH",
-      "referencia": "Especial (a contar dela mesma - Tipo D)",
-      "tipo_vencimento": "Término da Anterior",
-      "zera": "N",
-      "controle_original": "A O 6 H",
-      "intervalo_horas_voo": "400:00 HORAS DE VOO",
-      "intervalo_meses_continuos": "",
-      "intervalo_pousos": "",
-      "intervalos": [
-        "400:00 HORAS DE VOO"
-      ],
-      "linha_original": "7 CHECK 400 CHECK OPERACIONAL DE 400FH Especial (a contar dela mesma Término da Anterior N A O 6 H 400:00 HORAS DE VÔO  - Tipo D)"
-    },
-    {
-      "seq": 8,
-      "sigla_mnt": "1-INSP AOL",
-      "descricao_mnt": "INSPEÇÃO AOL 295-019",
-      "referencia": "Última inspeção",
-      "tipo_vencimento": "Término da Anterior",
-      "zera": "N",
-      "controle_original": "B 1 D",
-      "intervalo_horas_voo": "",
-      "intervalo_meses_continuos": "",
-      "intervalo_pousos": "3800 POUSOS",
-      "intervalos": [
-        "3800 POUSOS"
-      ],
-      "linha_original": "8 1-INSP AOL INSPEÇÃO AOL 295-019 Última inspeção Término da Anterior N B 1 D 3800 POUSOS"
-    },
-    {
-      "seq": 9,
-      "sigla_mnt": "2-INSP AOL",
-      "descricao_mnt": "INSPEÇÃO AOL 295-019",
-      "referencia": "Última Inspeção Específica 8 1-INSP AOL",
-      "tipo_vencimento": "Término da Anterior",
-      "zera": "N",
-      "controle_original": "B 1 D",
-      "intervalo_horas_voo": "",
-      "intervalo_meses_continuos": "",
-      "intervalo_pousos": "2000 POUSOS",
-      "intervalos": [
-        "2000 POUSOS"
-      ],
-      "linha_original": "9 2-INSP AOL INSPEÇÃO AOL 295-019 Última Inspeção Específica 8 1-INSP AOL Término da Anterior N B 1 D 2000 POUSOS"
-    },
-    {
-      "seq": 10,
-      "sigla_mnt": "3-INSP AOL",
-      "descricao_mnt": "INSPEÇÃO AOL 295-019",
-      "referencia": "Última Inspeção Específica 9 2-INSP AOL",
-      "tipo_vencimento": "Término da Anterior",
-      "zera": "N",
-      "controle_original": "B 1 D",
-      "intervalo_horas_voo": "",
-      "intervalo_meses_continuos": "",
-      "intervalo_pousos": "2000 POUSOS",
-      "intervalos": [
-        "2000 POUSOS"
-      ],
-      "linha_original": "10 3-INSP AOL INSPEÇÃO AOL 295-019 Última Inspeção Específica 9 2-INSP AOL Término da Anterior N B 1 D 2000 POUSOS"
-    },
-    {
-      "seq": 14,
-      "sigla_mnt": "INSP 8Y",
-      "descricao_mnt": "INSPEÇÃO CALENDÁRICA DE 8 YEARS",
-      "referencia": "Especial (a contar dela mesma - Tipo D)",
-      "tipo_vencimento": "Término da Anterior",
-      "zera": "N",
-      "controle_original": "B P 15 20 D",
-      "intervalo_horas_voo": "",
-      "intervalo_meses_continuos": "96 MESES CONTÍNUOS",
-      "intervalo_pousos": "",
-      "intervalos": [
-        "96 MESES CONTÍNUOS"
-      ],
-      "linha_original": "14 INSP 8Y INSPEÇÃO CALENDÁRICA DE 8 YEARS Especial (a contar dela mesma Término da Anterior N B P 15 20 D 96 MESES CONTÍNUOS  - Tipo D)"
-    },
-    {
-      "seq": 15,
-      "sigla_mnt": "INSP 1C",
-      "descricao_mnt": "INSPEÇÃO CHECK 1C",
-      "referencia": "Especial (a contar dela mesma - Tipo D)",
-      "tipo_vencimento": "Término da Anterior",
-      "zera": "N",
-      "controle_original": "C P 10 65 D",
-      "intervalo_horas_voo": "2400:00 HORAS DE VOO",
-      "intervalo_meses_continuos": "48 MESES CONTÍNUOS",
-      "intervalo_pousos": "",
-      "intervalos": [
-        "2400:00 HORAS DE VOO",
-        "48 MESES CONTÍNUOS"
-      ],
-      "linha_original": "15 INSP 1C INSPEÇÃO CHECK 1C Especial (a contar dela mesma Término da Anterior N C P 10 65 D 2400:00 HORAS DE VÔO  - Tipo D) 48 MESES CONTÍNUOS"
-    },
-    {
-      "seq": 16,
-      "sigla_mnt": "INSP 2C",
-      "descricao_mnt": "INSPEÇÃO GERAL CHECK 2C",
-      "referencia": "Especial (a contar dela mesma - Tipo D)",
-      "tipo_vencimento": "Término da Anterior",
-      "zera": "N",
-      "controle_original": "C P 60 10 D",
-      "intervalo_horas_voo": "4800:00 HORAS DE VOO",
-      "intervalo_meses_continuos": "96 MESES CONTÍNUOS",
-      "intervalo_pousos": "",
-      "intervalos": [
-        "4800:00 HORAS DE VOO",
-        "96 MESES CONTÍNUOS"
-      ],
-      "linha_original": "16 INSP 2C INSPEÇÃO GERAL CHECK 2C Especial (a contar dela mesma Término da Anterior N C P 60 10 D 4800:00 HORAS DE VÔO  - Tipo D) 96 MESES CONTÍNUOS"
-    },
-    {
-      "seq": 17,
-      "sigla_mnt": "INSP 4Y",
-      "descricao_mnt": "INSPEÇÃO CALENDÁRICA DE 4 YEARS",
-      "referencia": "Especial (a contar dela mesma - Tipo D)",
-      "tipo_vencimento": "Término da Anterior",
-      "zera": "N",
-      "controle_original": "B P 10 10 D",
-      "intervalo_horas_voo": "",
-      "intervalo_meses_continuos": "48 MESES CONTÍNUOS",
-      "intervalo_pousos": "",
-      "intervalos": [
-        "48 MESES CONTÍNUOS"
-      ],
-      "linha_original": "17 INSP 4Y INSPEÇÃO CALENDÁRICA DE 4 YEARS Especial (a contar dela mesma Término da Anterior N B P 10 10 D 48 MESES CONTÍNUOS  - Tipo D)"
-    },
-    {
-      "seq": 18,
-      "sigla_mnt": "INSP 100FH BR01",
-      "descricao_mnt": "INSPEÇÃO DE 100 FH",
-      "referencia": "Especial (a contar dela mesma - Tipo D)",
-      "tipo_vencimento": "Término da Anterior",
-      "zera": "N",
-      "controle_original": "O 1 H",
-      "intervalo_horas_voo": "100:00 HORAS DE VOO",
-      "intervalo_meses_continuos": "",
-      "intervalo_pousos": "",
-      "intervalos": [
-        "100:00 HORAS DE VOO"
-      ],
-      "linha_original": "18 INSP 100FH BR01 INSPEÇÃO DE 100 FH Especial (a contar dela mesma Término da Anterior N O 1 H 100:00 HORAS DE VÔO  - Tipo D)"
-    },
-    {
-      "seq": 19,
-      "sigla_mnt": "INSP 2400F BR01",
-      "descricao_mnt": "INPEÇÃO 2400FH",
-      "referencia": "Especial (a contar dela mesma - Tipo D)",
-      "tipo_vencimento": "Término da Anterior",
-      "zera": "N",
-      "controle_original": "O 3 H",
-      "intervalo_horas_voo": "2400:00 HORAS DE VOO",
-      "intervalo_meses_continuos": "",
-      "intervalo_pousos": "",
-      "intervalos": [
-        "2400:00 HORAS DE VOO"
-      ],
-      "linha_original": "19 INSP 2400F BR01 INPEÇÃO 2400FH Especial (a contar dela mesma Término da Anterior N O 3 H 2400:00 HORAS DE VÔO  - Tipo D)"
-    },
-    {
-      "seq": 20,
-      "sigla_mnt": "INSP 3000F BR01",
-      "descricao_mnt": "INSPEÇÃO 3000FH",
-      "referencia": "Especial (a contar dela mesma - Tipo D)",
-      "tipo_vencimento": "Término da Anterior",
-      "zera": "N",
-      "controle_original": "B 1 D",
-      "intervalo_horas_voo": "3000:00 HORAS DE VOO",
-      "intervalo_meses_continuos": "",
-      "intervalo_pousos": "",
-      "intervalos": [
-        "3000:00 HORAS DE VOO"
-      ],
-      "linha_original": "20 INSP 3000F BR01 INSPEÇÃO 3000FH Especial (a contar dela mesma Término da Anterior N B 1 D 3000:00 HORAS DE VÔO  - Tipo D)"
-    },
-    {
-      "seq": 22,
-      "sigla_mnt": "INSP 1000F",
-      "descricao_mnt": "INPEÇÃO 1000FH",
-      "referencia": "Especial (a contar dela mesma - Tipo D)",
-      "tipo_vencimento": "Término da Anterior",
-      "zera": "N",
-      "controle_original": "B 1 D",
-      "intervalo_horas_voo": "1000:00 HORAS DE VOO",
-      "intervalo_meses_continuos": "",
-      "intervalo_pousos": "",
-      "intervalos": [
-        "1000:00 HORAS DE VOO"
-      ],
-      "linha_original": "22 INSP 1000F INPEÇÃO 1000FH Especial (a contar dela mesma Término da Anterior N B 1 D 1000:00 HORAS DE VÔO  - Tipo D)"
-    },
-    {
-      "seq": 23,
-      "sigla_mnt": "INSP 50FH",
-      "descricao_mnt": "INSPEÇÃO 50 FH",
-      "referencia": "Especial (a contar dela mesma - Tipo D)",
-      "tipo_vencimento": "Término da Anterior",
-      "zera": "N",
-      "controle_original": "B 1 D",
-      "intervalo_horas_voo": "50:00 HORAS DE VOO",
-      "intervalo_meses_continuos": "",
-      "intervalo_pousos": "",
-      "intervalos": [
-        "50:00 HORAS DE VOO"
-      ],
-      "linha_original": "23 INSP 50FH INSPEÇÃO 50 FH Especial (a contar dela mesma Término da Anterior N B 1 D 50:00 HORAS DE VÔO  - Tipo D)"
-    }
-  ]
-}

pre_process/documento_joaomarcos_texto.txt

@@ -1,46 +0,0 @@
-SISTEMA INTEGRADO DE LOGÍSTICA DE MATERIAL E DE SERVIÇOS Pág.: 1 de 1
-BASE AÉREA DE MANAUS Data: 15/06/2026
-Hora: 13:23:38
-RELATÓRIO DE CICLO DE INSPEÇÕES DO EQUIPAMENTO
-CTR0142R v.10.5
-PN: ANV C-105 CFF: 0117B Nomenclatura: AERONAVE, ASA FIXA, CASA 295 (C-105 AMAZONAS)
-SN: 038 Matrícula: 2805 Modelo: C-105 Ciclo Atual: 3650007082
-Seq. Sigla da MNT Descrição da MNT Referência Referência Tipo"E" Vencimento Zera TSO Letra Nível Var. Média Duração Intervalo Controle
-2 INSP 1A INSPEÇÃO CHECK 1A Especial (a contar dela mesma Término da Anterior N A B 3 3 D 300:00 HORAS DE VÔO
-- Tipo D)
-8 MESES CONTÍNUOS
-3 INSP 2A INSPEÇÃO CHECK 2A Especial (a contar dela mesma Término da Anterior N A B 10 4 D 600:00 HORAS DE VÔO
-- Tipo D)
-16 MESES CONTÍNUOS
-4 INSP 3A INSPEÇÃO CHECK 3A Especial (a contar dela mesma Término da Anterior N A B 10 5 D 900:00 HORAS DE VÔO
-- Tipo D)
-24 MESES CONTÍNUOS
-5 INSP 2Y INSPEÇÃO CALENDÁRICA DE 2 YEARS Especial (a contar dela mesma Término da Anterior N B B 5 5 D 24 MESES CONTÍNUOS
-- Tipo D)
-6 CHECK 300 CHECK OPERACIONAL DE 300FH Especial (a contar dela mesma Término da Anterior N A O 6 H 300:00 HORAS DE VÔO
-- Tipo D)
-7 CHECK 400 CHECK OPERACIONAL DE 400FH Especial (a contar dela mesma Término da Anterior N A O 6 H 400:00 HORAS DE VÔO
-- Tipo D)
-8 1-INSP AOL INSPEÇÃO AOL 295-019 Última inspeção Término da Anterior N B 1 D 3800 POUSOS
-9 2-INSP AOL INSPEÇÃO AOL 295-019 Última Inspeção Específica 8 1-INSP AOL Término da Anterior N B 1 D 2000 POUSOS
-10 3-INSP AOL INSPEÇÃO AOL 295-019 Última Inspeção Específica 9 2-INSP AOL Término da Anterior N B 1 D 2000 POUSOS
-14 INSP 8Y INSPEÇÃO CALENDÁRICA DE 8 YEARS Especial (a contar dela mesma Término da Anterior N B P 15 20 D 96 MESES CONTÍNUOS
-- Tipo D)
-15 INSP 1C INSPEÇÃO CHECK 1C Especial (a contar dela mesma Término da Anterior N C P 10 65 D 2400:00 HORAS DE VÔO
-- Tipo D)
-48 MESES CONTÍNUOS
-16 INSP 2C INSPEÇÃO GERAL CHECK 2C Especial (a contar dela mesma Término da Anterior N C P 60 10 D 4800:00 HORAS DE VÔO
-- Tipo D)
-96 MESES CONTÍNUOS
-17 INSP 4Y INSPEÇÃO CALENDÁRICA DE 4 YEARS Especial (a contar dela mesma Término da Anterior N B P 10 10 D 48 MESES CONTÍNUOS
-- Tipo D)
-18 INSP 100FH BR01 INSPEÇÃO DE 100 FH Especial (a contar dela mesma Término da Anterior N O 1 H 100:00 HORAS DE VÔO
-- Tipo D)
-19 INSP 2400F BR01 INPEÇÃO 2400FH Especial (a contar dela mesma Término da Anterior N O 3 H 2400:00 HORAS DE VÔO
-- Tipo D)
-20 INSP 3000F BR01 INSPEÇÃO 3000FH Especial (a contar dela mesma Término da Anterior N B 1 D 3000:00 HORAS DE VÔO
-- Tipo D)
-22 INSP 1000F INPEÇÃO 1000FH Especial (a contar dela mesma Término da Anterior N B 1 D 1000:00 HORAS DE VÔO
-- Tipo D)
-23 INSP 50FH INSPEÇÃO 50 FH Especial (a contar dela mesma Término da Anterior N B 1 D 50:00 HORAS DE VÔO
-- Tipo D)

pre_process/preprocess_pdf.py

@@ -1,181 +0,0 @@
-import csv
-import json
-import re
-from pathlib import Path
-
-import pdfplumber
-
-
-ROOT = Path(__file__).resolve().parents[1]
-PDF_PATH = ROOT / "raw" / "documento joaomarcos.pdf"
-TXT_PATH = ROOT / "pre_process" / "documento_joaomarcos_texto.txt"
-JSON_PATH = ROOT / "pre_process" / "documento_joaomarcos_inspecoes.json"
-CSV_PATH = ROOT / "pre_process" / "documento_joaomarcos_inspecoes.csv"
-
-ROW_START_RE = re.compile(r"^(?P<seq>\d+)\s+")
-INTERVAL_RE = re.compile(
-    r"(?P<intervalo>\d+(?::\d+)?\s*(?:HORAS DE V[ÔO]O|MESES CONT[ÍI]NUOS|POUSOS))",
-    re.IGNORECASE,
-)
-
-
-def extract_text(pdf_path):
-    pages = []
-    with pdfplumber.open(pdf_path) as pdf:
-        for page in pdf.pages:
-            pages.append(page.extract_text(x_tolerance=1, y_tolerance=3) or "")
-    return "\n\n".join(pages).strip()
-
-
-def normalize_interval(value):
-    return re.sub(r"\s+", " ", value.replace("VÔO", "VOO")).strip()
-
-
-def split_records(text):
-    records = []
-    current = []
-
-    for raw_line in text.splitlines():
-        line = re.sub(r"\s+", " ", raw_line).strip()
-        if not line:
-            continue
-        if ROW_START_RE.match(line) and " Término da Anterior " in line:
-            if current:
-                records.append(" ".join(current))
-            current = [line]
-        elif current:
-            current.append(line)
-
-    if current:
-        records.append(" ".join(current))
-
-    return records
-
-
-def parse_header(text):
-    header = {}
-
-    patterns = {
-        "data_relatorio": r"Data:\s*(\d{2}/\d{2}/\d{4})",
-        "hora_relatorio": r"Hora:\s*(\d{2}:\d{2}:\d{2})",
-        "pn": r"PN:\s*(.*?)\s+CFF:",
-        "cff": r"CFF:\s*(.*?)\s+Nomenclatura:",
-        "nomenclatura": r"Nomenclatura:\s*(.*?)\s+SN:",
-        "sn": r"SN:\s*(.*?)\s+Matrícula:",
-        "matricula": r"Matrícula:\s*(.*?)\s+Modelo:",
-        "modelo": r"Modelo:\s*(.*?)\s+Ciclo Atual:",
-        "ciclo_atual": r"Ciclo Atual:\s*(\S+)",
-    }
-
-    compact_text = re.sub(r"\s+", " ", text)
-    for key, pattern in patterns.items():
-        match = re.search(pattern, compact_text)
-        if match:
-            header[key] = match.group(1).strip()
-
-    return header
-
-
-def parse_record(record):
-    record = record.replace("- Tipo D)", " - Tipo D)")
-    has_tipo_d = " - Tipo D)" in record
-    left, right = record.split(" Término da Anterior ", 1)
-
-    seq_match = ROW_START_RE.match(left)
-    seq = int(seq_match.group("seq"))
-    left_body = left[seq_match.end() :]
-
-    intervalos = [normalize_interval(m.group("intervalo")) for m in INTERVAL_RE.finditer(right)]
-    right_before_interval = INTERVAL_RE.split(right, maxsplit=1)[0].strip()
-    tokens = right_before_interval.split()
-
-    zera = tokens[0] if tokens else ""
-    controle = " ".join(tokens[1:])
-
-    especial_marker = " Especial (a contar dela mesma"
-    if especial_marker in left_body:
-        before_ref, referencia = left_body.split(especial_marker, 1)
-        referencia = "Especial (a contar dela mesma"
-        if has_tipo_d:
-            referencia += " - Tipo D)"
-    else:
-        ref_match = re.search(r"(Última .*)$", left_body)
-        referencia = ref_match.group(1).strip() if ref_match else ""
-        before_ref = left_body[: ref_match.start()].strip() if ref_match else left_body
-
-    upper_before_ref = before_ref.upper()
-    desc_idx = upper_before_ref.find("INSPEÇÃO")
-    if desc_idx == -1:
-        desc_idx = upper_before_ref.find("INPEÇÃO")
-    if desc_idx == -1:
-        desc_idx = upper_before_ref.find("CHECK OPERACIONAL")
-
-    if desc_idx != -1:
-        sigla = before_ref[:desc_idx].strip()
-        descricao = before_ref[desc_idx:].strip()
-    else:
-        parts = before_ref.split(maxsplit=1)
-        sigla = parts[0] if parts else ""
-        descricao = parts[1] if len(parts) > 1 else ""
-
-    intervalo_horas_voo = next((item for item in intervalos if "HORAS DE VOO" in item), "")
-    intervalo_meses_continuos = next((item for item in intervalos if "MESES CONTÍNUOS" in item), "")
-    intervalo_pousos = next((item for item in intervalos if "POUSOS" in item), "")
-
-    return {
-        "seq": seq,
-        "sigla_mnt": sigla,
-        "descricao_mnt": descricao,
-        "referencia": referencia,
-        "tipo_vencimento": "Término da Anterior",
-        "zera": zera,
-        "controle_original": controle,
-        "intervalo_horas_voo": intervalo_horas_voo,
-        "intervalo_meses_continuos": intervalo_meses_continuos,
-        "intervalo_pousos": intervalo_pousos,
-        "intervalos": intervalos,
-        "linha_original": record,
-    }
-
-
-def main():
-    text = extract_text(PDF_PATH)
-    TXT_PATH.write_text(text + "\n", encoding="utf-8")
-
-    payload = {
-        "fonte": str(PDF_PATH.relative_to(ROOT)),
-        "metadados": parse_header(text),
-        "inspecoes": [parse_record(record) for record in split_records(text)],
-    }
-
-    JSON_PATH.write_text(json.dumps(payload, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
-
-    fields = [
-        "seq",
-        "sigla_mnt",
-        "descricao_mnt",
-        "referencia",
-        "tipo_vencimento",
-        "zera",
-        "controle_original",
-        "intervalo_horas_voo",
-        "intervalo_meses_continuos",
-        "intervalo_pousos",
-        "intervalos",
-    ]
-    with CSV_PATH.open("w", newline="", encoding="utf-8-sig") as csv_file:
-        writer = csv.DictWriter(csv_file, fieldnames=fields, delimiter=";")
-        writer.writeheader()
-        for row in payload["inspecoes"]:
-            csv_row = {key: row[key] for key in fields}
-            csv_row["intervalos"] = " | ".join(row["intervalos"])
-            writer.writerow(csv_row)
-
-    print(f"Gerado: {TXT_PATH.relative_to(ROOT)}")
-    print(f"Gerado: {JSON_PATH.relative_to(ROOT)}")
-    print(f"Gerado: {CSV_PATH.relative_to(ROOT)}")
-    print(f"Inspeções extraídas: {len(payload['inspecoes'])}")
-
-
-if __name__ == "__main__":
-    main()

raw/AERONAVES.csv

@@ -0,0 +1,5 @@
+MATRICULA;MODELO;FH TOTAIS
+FAB2800;C105;295
+FAB2803;C105;275
+FAB2809;C105;265
+FAB2811;C105;255

raw/AIRPORTS.csv

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+AIRPORT_CODE;AIRPORT_NAME;LATITUDE;LONGITUDE;IS_MAINTENANCE_BASE;COUNTRY
+SBMN;Manaus - Eduardo Gomes;-3.0267;-60.0496;1;Brazil
+SBBR;Brasília - Presidente Juscelino Kubitschek;-15.7942;-47.8822;0;Brazil
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+SBCT;Curitiba - Afonso Pena;-25.4844;-49.3220;0;Brazil
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+SBRF;Recife - Guararapes;-8.2645;-35.2426;0;Brazil
+SBBV;Boa Vista - Atlas Brasil Cantá;2.8420;-60.6942;0;Brazil
+SBSN;São Gabriel da Cachoeira;-0.1333;-67.0833;0;Brazil
+SBTS;Tabatinga;-4.2505;-69.9342;0;Brazil
+SBMQ;Maués;-3.3900;-61.2942;0;Brazil
+SBBE;Benjamin Constant;-4.3797;-70.0336;0;Brazil
+SBOI;Iquitos - Coronel FAP Vasquez;-3.7458;-73.3075;0;Peru
+SBPV;Porto Velho - Governador Jorge Teixeira;-8.7608;-63.9047;0;Brazil
+SBVH;Vilhena;-12.7408;-60.1447;0;Brazil
+SBGL;Rio de Janeiro - Galeão;-22.8097;-43.2506;0;Brazil
+SBCY;Cruzeiro do Sul - Aeroporto;-7.6258;-72.6647;0;Brazil
+SBCO;Comodoro - Aeroporto;-20.4753;-58.6753;0;Brazil
+SBLO;Londrina - Governador Roberto Silveira;-23.3263;-51.1267;0;Brazil
+SBTF;Teresina - Senador Petrônio Portela;-5.0589;-42.8229;0;Brazil
+SBTT;Tatooine - Regional;-7.2100;-74.5700;0;Brazil
+SBUA;Uaupes;0.8333;-70.5667;0;Brazil
+SBUY;Uruçuí;-7.1833;-44.3833;0;Brazil
+SBYS;Yacuanã;0.7000;-67.0667;0;Brazil
+SWBC;Barcelos;0.9806;-62.9461;0;Brazil
+SWCA;Carauari;-4.8808;-66.8858;0;Brazil
+SWEI;Envira;-7.5636;-70.0258;0;Brazil
+SWKO;Kopena;-5.0500;-67.5000;0;Brazil
+SBSM;Santa Maria;-29.7108;-53.6922;0;Brazil
+SBCC;Guarantã do Norte (CPBV);-9.3333;-54.9647;0;Brazil
+SBAN;Anápolis (Campo Marechal Márcio);-16.2386;-48.9722;0;Brazil

raw/CHECKS.csv

@@ -0,0 +1,5 @@
+CHECKS;FH;TEMPO DE EXECUCAO (DIAS);LOCAL DE EXECUCAO
+CHECK 1A_OPERACIONAL 300FH;300;3;SBMN
+OPEREACIONAL 400FH;400;1;SBMN
+CHECK 2A_OPERACIONAL 600FH;600;4;SBMN
+CHECK 3A;900;5;SBMN

raw/ESCALA DE VOO MODELO 1.csv

@@ -0,0 +1,64 @@
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requirements.txt

@@ -0,0 +1,11 @@
+pulp>=2.7.0
+pandas>=2.0.0
+numpy>=1.24.0
+streamlit>=1.28.0
+plotly>=5.17.0
+openpyxl>=3.1.0
+networkx>=3.2.0
+scipy>=1.11.0
+airportsdata>=20240101
+folium>=0.14.0
+streamlit-folium>=0.22.0

run_pipeline.bat

@@ -0,0 +1,37 @@
+@echo off
+echo ============================================================
+echo  OARMP – Aircraft Routing and Maintenance Planning Pipeline
+echo ============================================================
+cd /d "%~dp0"
+
+echo.
+echo [00] Setup...
+.venv\Scripts\python.exe scripts\00_setup_project.py
+if %ERRORLEVEL% neq 0 ( echo ERRO no setup & pause & exit /b 1 )
+
+echo.
+echo [01] Inspecionando arquivos...
+.venv\Scripts\python.exe scripts\01_inspect_inputs.py
+if %ERRORLEVEL% neq 0 ( echo ERRO na inspecao & pause & exit /b 1 )
+
+echo.
+echo [02] Construindo referencia de frota...
+.venv\Scripts\python.exe scripts\02_build_fleet_reference.py
+if %ERRORLEVEL% neq 0 ( echo ERRO na frota & pause & exit /b 1 )
+
+echo.
+echo [03] Executando otimizacao...
+.venv\Scripts\python.exe scripts\03_run_optimization_pipeline.py
+if %ERRORLEVEL% neq 0 ( echo ERRO na otimizacao & pause & exit /b 1 )
+
+echo.
+echo [04] Gerando flight strings...
+.venv\Scripts\python.exe scripts\04_generate_flight_strings.py
+if %ERRORLEVEL% neq 0 ( echo ERRO na geracao & pause & exit /b 1 )
+
+echo.
+echo ============================================================
+echo  Pipeline concluido com sucesso!
+echo  Para visualizar o dashboard: scripts\05_run_dashboard.bat
+echo ============================================================
+pause

scripts/00_setup_project.py

@@ -0,0 +1,50 @@
+"""
+Script 00 – Project setup.
+
+Creates all required directories and validates that the raw input files exist.
+Run once after cloning the repository.
+"""
+
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from src.routing_engine.config import DEFAULT_CONFIG
+
+cfg = DEFAULT_CONFIG
+
+DIRS = [
+    cfg.raw_index_dir,
+    cfg.processed_dir,
+    cfg.reference_dir,
+    cfg.quality_dir,
+    cfg.schedules_dir,
+    cfg.figures_dir,
+    cfg.exports_dir,
+    ROOT / "outputs" / "dashboards",
+]
+
+print("Creating project directories…")
+for d in DIRS:
+    d.mkdir(parents=True, exist_ok=True)
+    print(f"  ok  {d.relative_to(ROOT)}")
+
+print("\nChecking raw input files…")
+REQUIRED = [cfg.flight_schedule_file, cfg.aircraft_file, cfg.checks_file, cfg.airports_file]
+all_ok = True
+for fname in REQUIRED:
+    p = cfg.raw_dir / fname
+    if p.exists():
+        size = p.stat().st_size
+        print(f"  ok  {fname}  ({size:,} bytes)")
+    else:
+        print(f"  ✗  {fname}  NOT FOUND")
+        all_ok = False
+
+if all_ok:
+    print("\n[OK]  Setup complete.  Run scripts in order: 01 -> 02 -> 03 -> 04.")
+else:
+    print("\n[!]  Some raw files are missing. Place them in the 'raw/' folder and re-run.")
+    sys.exit(1)

scripts/01_inspect_inputs.py

@@ -0,0 +1,41 @@
+"""
+Script 01 – Inspect raw input files.
+
+Prints metadata (encoding, separator, column names, sample rows) for every CSV
+in the raw/ folder and saves a JSON report to data/raw_index/.
+"""
+
+import json
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from src.routing_engine.config import DEFAULT_CONFIG
+from src.routing_engine.inspect_files import inspect_all
+
+cfg = DEFAULT_CONFIG
+
+print(f"Inspecting files in: {cfg.raw_dir}\n")
+meta = inspect_all(cfg.raw_dir)
+
+for name, info in meta.items():
+    print(f"-- {name} -------------------------------------------")
+    if "error" in info:
+        print(f"   ERROR: {info['error']}")
+        continue
+    print(f"   Encoding  : {info['encoding']}")
+    print(f"   Separator : {repr(info['separator'])}")
+    print(f"   Columns   : {info['columns']}")
+    if info["sample_rows"]:
+        print(f"   First row : {info['sample_rows'][0]}")
+    print()
+
+# Save report
+out = cfg.raw_index_dir / "file_inventory.json"
+cfg.raw_index_dir.mkdir(parents=True, exist_ok=True)
+with open(out, "w", encoding="utf-8") as f:
+    json.dump(meta, f, ensure_ascii=False, indent=2, default=str)
+
+print(f"Report saved -> {out}")

scripts/02_build_fleet_reference.py

@@ -0,0 +1,64 @@
+"""
+Script 02 – Build fleet reference table.
+
+Reads AERONAVES.csv + CHECKS.csv, computes TTM and the full check-cycle
+sequence for each aircraft, and saves the reference to data/reference/.
+"""
+
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+import pandas as pd
+from datetime import datetime
+
+from src.routing_engine.config import DEFAULT_CONFIG
+from src.routing_engine.inspect_files import read_aircraft, read_checks
+from src.routing_engine.ingest import build_fleet, _check_cycles
+
+cfg = DEFAULT_CONFIG
+
+aircraft_df = read_aircraft(cfg.raw_dir / cfg.aircraft_file)
+checks_df = read_checks(cfg.raw_dir / cfg.checks_file)
+
+print("Aircraft loaded:")
+print(aircraft_df.to_string(index=False))
+print()
+
+print("Checks loaded:")
+print(checks_df.to_string(index=False))
+print()
+
+# Build fleet
+planning_start = datetime(cfg.planning_year, 3, 18)  # first OFRAG departure in the sample data
+fleet = build_fleet(aircraft_df, checks_df, planning_start)
+
+print("Fleet reference:")
+for _, row in fleet.iterrows():
+    print(f"\n  {row['tail_number']}  ({row['model']})  FH total = {row['fh_total']:.0f}")
+    print(f"    TTM before first check: {row['ttm_hours']:.1f} FH")
+    for i, c in enumerate(row["checks"]):
+        print(f"    Cycle {i}: threshold={c['fh_threshold']:.0f} FH, "
+              f"TTM={c['ttm']:.0f} FH, duration={c['duration_hours']:.0f} h")
+
+# Save to reference
+cfg.reference_dir.mkdir(parents=True, exist_ok=True)
+out_path = cfg.reference_dir / "fleet_reference.csv"
+
+flat_rows = []
+for _, row in fleet.iterrows():
+    flat_rows.append(
+        {
+            "tail_number": row["tail_number"],
+            "model": row["model"],
+            "fh_total": row["fh_total"],
+            "ttm_hours_cycle0": row["ttm_hours"],
+            "n_check_cycles": len(row["checks"]),
+            "cycles_json": str(row["checks"]),
+        }
+    )
+
+pd.DataFrame(flat_rows).to_csv(out_path, index=False)
+print(f"\nFleet reference saved -> {out_path}")

scripts/03_run_optimization_pipeline.py

@@ -0,0 +1,44 @@
+"""
+Script 03 – Run the full optimisation pipeline.
+
+Loads data, builds the network, runs Column Generation + B&B, and prints
+the solution.  All outputs are saved to outputs/ automatically.
+"""
+
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from src.routing_engine import RoutingPipeline, DEFAULT_CONFIG
+
+cfg = DEFAULT_CONFIG
+# Adjust parameters here if needed:
+# cfg.tat_minutes = 60
+# cfg.mip_time_limit_seconds = 120
+
+pipe = RoutingPipeline(cfg)
+result = pipe.run(save_outputs=True)
+
+print("\n" + "=" * 60)
+print("SOLUTION SUMMARY")
+print("=" * 60)
+s = result["summary"]
+for k, v in s.items():
+    print(f"  {k:<35} {v}")
+
+print("\n-- Schedule ----------------------------------------------")
+sched = result["schedule"]
+if not sched.empty:
+    print(sched[["aircraft", "ofrag_id", "departure", "arrival", "flight_hours", "maintenance_before"]].to_string(index=False))
+
+print("\n-- Maintenance events -------------------------------------")
+maint = result["maintenance"]
+if maint.empty:
+    print("  No forced maintenance events.")
+else:
+    print(maint.to_string(index=False))
+
+print("\n-- Fleet utilisation --------------------------------------")
+print(result["fleet"].to_string(index=False))

scripts/04_generate_flight_strings.py

@@ -0,0 +1,75 @@
+"""
+Script 04 – Generate and export flight strings.
+
+Reads the saved solution CSVs and generates a per-aircraft "flight string"
+report in human-readable format (console + Excel export).
+"""
+
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+import pandas as pd
+
+from src.routing_engine.config import DEFAULT_CONFIG
+
+cfg = DEFAULT_CONFIG
+
+sched_path = cfg.schedules_dir / "flight_strings.csv"
+maint_path = cfg.schedules_dir / "maintenance_events.csv"
+
+if not sched_path.exists():
+    print("No schedule found. Run script 03 first.")
+    sys.exit(1)
+
+sched = pd.read_csv(sched_path, parse_dates=["departure", "arrival"])
+maint = pd.read_csv(maint_path, parse_dates=["maint_start", "maint_end"]) if maint_path.exists() else pd.DataFrame()
+
+print("=" * 70)
+print("FLIGHT STRINGS PER AIRCRAFT")
+print("=" * 70)
+
+for aircraft, group in sched.groupby("aircraft"):
+    print(f"\n{'-'*70}")
+    print(f"  Aeronave: {aircraft}")
+    print(f"{'-'*70}")
+
+    total_fh = 0.0
+    for _, row in group.sort_values("departure").iterrows():
+        maint_flag = " <- MANUTENCAO ANTES" if row.get("maintenance_before") else ""
+        dep = pd.to_datetime(row["departure"])
+        arr = pd.to_datetime(row["arrival"])
+        fh = float(row.get("flight_hours", 0))
+        total_fh += fh
+        print(
+            f"    {row['ofrag_id']:12s}  "
+            f"{dep.strftime('%d/%m %H:%M')} -> {arr.strftime('%d/%m %H:%M')}  "
+            f"({fh:.2f} FH){maint_flag}"
+        )
+
+    print(f"    {'TOTAL':12s}  {total_fh:.2f} FH")
+
+    if not maint.empty:
+        ac_maint = maint[maint["aircraft"] == aircraft]
+        if not ac_maint.empty:
+            print(f"\n  Eventos de manutencao:")
+            for _, ev in ac_maint.iterrows():
+                print(
+                    f"    CHECK (ciclo {int(ev['check_cycle_index'])})  "
+                    f"{pd.to_datetime(ev['maint_start']).strftime('%d/%m %H:%M')} -> "
+                    f"{pd.to_datetime(ev['maint_end']).strftime('%d/%m %H:%M')}  "
+                    f"TTM perdido: {ev['ttm_loss_hours']:.2f} FH"
+                )
+
+# Export to Excel
+out_xlsx = cfg.exports_dir / "flight_strings.xlsx"
+try:
+    with pd.ExcelWriter(out_xlsx, engine="openpyxl") as writer:
+        sched.to_excel(writer, sheet_name="Escala", index=False)
+        if not maint.empty:
+            maint.to_excel(writer, sheet_name="Manutencao", index=False)
+    print(f"\n\nExportado para Excel -> {out_xlsx}")
+except ImportError:
+    print("\n(openpyxl not installed – Excel export skipped)")

scripts/05_run_dashboard.bat

@@ -0,0 +1,5 @@
+@echo off
+echo Iniciando dashboard OARMP...
+cd /d "%~dp0.."
+.venv\Scripts\streamlit.exe run app\dashboard.py --server.port 8501
+pause

src/routing_engine/__init__.py

@@ -0,0 +1,6 @@
+"""Aircraft Routing Engine – Set Partitioning / Column Generation / Branch & Bound."""
+
+from .config import RoutingConfig, DEFAULT_CONFIG
+from .pipeline import RoutingPipeline
+
+__all__ = ["RoutingConfig", "DEFAULT_CONFIG", "RoutingPipeline"]

src/routing_engine/config.py

@@ -0,0 +1,74 @@
+"""Centralised configuration for the routing engine."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from datetime import datetime
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+
+
+@dataclass
+class RoutingConfig:
+    # ── Paths ──────────────────────────────────────────────────────────────────
+    project_root: Path = field(default_factory=lambda: PROJECT_ROOT)
+
+    @property
+    def raw_dir(self) -> Path:
+        return self.project_root / "raw"
+
+    @property
+    def raw_index_dir(self) -> Path:
+        return self.project_root / "data" / "raw_index"
+
+    @property
+    def processed_dir(self) -> Path:
+        return self.project_root / "data" / "processed"
+
+    @property
+    def reference_dir(self) -> Path:
+        return self.project_root / "data" / "reference"
+
+    @property
+    def quality_dir(self) -> Path:
+        return self.project_root / "data" / "quality"
+
+    @property
+    def schedules_dir(self) -> Path:
+        return self.project_root / "outputs" / "schedules"
+
+    @property
+    def figures_dir(self) -> Path:
+        return self.project_root / "outputs" / "figures"
+
+    @property
+    def exports_dir(self) -> Path:
+        return self.project_root / "outputs" / "exports"
+
+    # ── File names (auto-detected if left as empty string) ─────────────────────
+    flight_schedule_file: str = "ESCALA DE VOO MODELO 1.csv"
+    aircraft_file: str = "AERONAVES.csv"
+    checks_file: str = "CHECKS.csv"
+    airports_file: str = "AIRPORTS.csv"
+
+    # ── Calendar / planning ────────────────────────────────────────────────────
+    planning_year: int = field(default_factory=lambda: datetime.now().year)
+    maintenance_base_code: str = "SBMN"
+
+    # ── Operational constraints ────────────────────────────────────────────────
+    tat_minutes: int = 60          # minimum turnaround time between OFRAGs
+    aircraft_availability_offset_hours: int = 0  # hours before first OFRAG aircraft is available
+
+    # ── Optimiser parameters ───────────────────────────────────────────────────
+    big_m: float = 1e6             # penalty for uncovered OFRAGs (artificial variable)
+    cg_tolerance: float = 1e-6     # column-generation stopping threshold on reduced cost
+    max_cg_iterations: int = 200
+    mip_time_limit_seconds: int = 300
+    mip_gap: float = 0.0           # optimality gap (0 = exact)
+
+    # ── Logging ────────────────────────────────────────────────────────────────
+    log_level: str = "INFO"
+
+
+DEFAULT_CONFIG = RoutingConfig()

src/routing_engine/ingest.py

@@ -0,0 +1,322 @@
+"""
+Data ingestion layer.
+
+Reads raw files, parses dates/times, aggregates legs into OFRAG profiles,
+and computes TTM (Time to Maintenance) per aircraft.
+"""
+
+from __future__ import annotations
+
+import logging
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import pandas as pd
+
+from .config import RoutingConfig
+from .inspect_files import read_aircraft, read_checks, read_airports, read_schedule
+
+logger = logging.getLogger(__name__)
+
+_BR_MONTHS = {
+    "jan": 1, "fev": 2, "mar": 3, "abr": 4,
+    "mai": 5, "jun": 6, "jul": 7, "ago": 8,
+    "set": 9, "out": 10, "nov": 11, "dez": 12,
+}
+
+
+# ── Low-level parsers ─────────────────────────────────────────────────────────
+
+def _parse_br_date(date_str: str, year: int) -> Optional[datetime]:
+    """Parse 'DD/MM/AAAA' or legacy 'DD/mon' → datetime."""
+    try:
+        parts = date_str.strip().split("/")
+        day = int(parts[0])
+        if len(parts) == 3:  # DD/MM/AAAA
+            return datetime(int(parts[2]), int(parts[1]), day)
+        # Legacy: DD/mon  (uses fallback year from config)
+        mon = _BR_MONTHS.get(parts[1].lower(), 0)
+        return datetime(year, mon, day) if mon else None
+    except Exception:
+        return None
+
+
+def _parse_time(time_str: str) -> Optional[Tuple[int, int]]:
+    """Parse 'HH:MM' or 'HH:MM:SS' → (hour, minute).  Returns None on failure."""
+    try:
+        parts = time_str.strip().split(":")
+        return int(parts[0]), int(parts[1])
+    except Exception:
+        return None
+
+
+def _parse_flight_hours(tempo_str: str) -> float:
+    """Convert 'HH:MM' flight-time string → decimal hours."""
+    try:
+        parts = tempo_str.strip().split(":")
+        return int(parts[0]) + int(parts[1]) / 60.0
+    except Exception:
+        return 0.0
+
+
+# ── Schedule parsing ──────────────────────────────────────────────────────────
+
+def _parse_schedule_datetimes(df: pd.DataFrame, year: int) -> pd.DataFrame:
+    """
+    Add DATETIME_DEP and DATETIME_ARR columns to the schedule DataFrame.
+
+    The DATA column holds the departure date; arrival is the same date unless
+    the arrival time string contains a second ':' colon (e.g. '00:30:00'),
+    which signals midnight crossing → arrival is departure date + 1 day.
+    """
+    dep_dts, arr_dts, fh_vals = [], [], []
+
+    for _, row in df.iterrows():
+        base_date = _parse_br_date(str(row["DATA"]), year)
+        if base_date is None:
+            dep_dts.append(pd.NaT)
+            arr_dts.append(pd.NaT)
+            fh_vals.append(0.0)
+            continue
+
+        dep_t = _parse_time(str(row["HORA_DEP"]))
+        arr_str = str(row["HORA_ARR"])
+        arr_t = _parse_time(arr_str)
+
+        if dep_t is None or arr_t is None:
+            dep_dts.append(pd.NaT)
+            arr_dts.append(pd.NaT)
+            fh_vals.append(0.0)
+            continue
+
+        dep_dt = base_date.replace(hour=dep_t[0], minute=dep_t[1])
+        arr_dt = base_date.replace(hour=arr_t[0], minute=arr_t[1])
+
+        # Midnight crossing: arrival string has 3 colon-separated parts OR arr <= dep
+        midnight_flag = arr_str.count(":") >= 2 or arr_dt <= dep_dt
+        if midnight_flag:
+            arr_dt += timedelta(days=1)
+
+        dep_dts.append(dep_dt)
+        arr_dts.append(arr_dt)
+        fh_vals.append(_parse_flight_hours(str(row["TEMPO_VOO"])))
+
+    df = df.copy()
+    df["DATETIME_DEP"] = dep_dts
+    df["DATETIME_ARR"] = arr_dts
+    df["FH_LEG"] = fh_vals
+    return df.dropna(subset=["DATETIME_DEP", "DATETIME_ARR"]).reset_index(drop=True)
+
+
+# ── OFRAG aggregation ─────────────────────────────────────────────────────────
+
+def build_ofrags(schedule_df: pd.DataFrame, base_codes: List[str]) -> pd.DataFrame:
+    """
+    Group schedule legs by OFRAG number and produce one row per OFRAG with:
+    ofrag_id, departure (first leg dep at base), arrival (last leg arr at base),
+    flight_hours (sum), origin, destination, starts_at_base, ends_at_base.
+    """
+    records = []
+    for ofrag_num, group in schedule_df.groupby("OFRAG", sort=False):
+        group = group.sort_values("DATETIME_DEP").reset_index(drop=True)
+
+        first = group.iloc[0]
+        last = group.iloc[-1]
+
+        total_fh = group["FH_LEG"].sum()
+        origin = str(first["DEP"]).strip().upper()
+        destination = str(last["ARR"]).strip().upper()
+
+        starts_base = origin in base_codes
+        ends_base = destination in base_codes
+
+        records.append(
+            {
+                "ofrag_id": f"OFRAG{int(ofrag_num):03d}",
+                "ofrag_num": int(ofrag_num),
+                "departure": first["DATETIME_DEP"],
+                "arrival": last["DATETIME_ARR"],
+                "flight_hours": round(total_fh, 3),
+                "origin": origin,
+                "destination": destination,
+                "starts_at_base": starts_base,
+                "ends_at_base": ends_base,
+                "n_legs": len(group),
+                "missions": ",".join(group["MISSAO"].dropna().unique()),
+            }
+        )
+
+    df = pd.DataFrame(records).sort_values("departure").reset_index(drop=True)
+    logger.info(
+        "Built %d OFRAGs  (%d start+end at base)",
+        len(df),
+        df["starts_at_base"].sum() & df["ends_at_base"].sum(),
+    )
+    return df
+
+
+# ── TTM computation ────────────────────────────────────────────────────────────
+
+def _check_cycles(current_fh: float, thresholds: List[float], durations_days: List[float]) -> List[Dict]:
+    """
+    Return the ordered sequence of upcoming maintenance checks for an aircraft
+    currently at *current_fh* total flight hours.
+
+    Each entry: {'fh_threshold': …, 'ttm': …, 'duration_hours': …}
+    The first entry is the immediately upcoming check; subsequent entries follow.
+    """
+    # Sort checks by threshold
+    paired = sorted(zip(thresholds, durations_days), key=lambda x: x[0])
+    cycles = []
+    prev_threshold = current_fh
+    for threshold, days in paired:
+        if threshold > current_fh:
+            ttm = threshold - prev_threshold if cycles else threshold - current_fh
+            cycles.append(
+                {
+                    "fh_threshold": threshold,
+                    "ttm": ttm,
+                    "duration_hours": days * 24.0,
+                }
+            )
+            prev_threshold = threshold
+
+    # Add a synthetic final cycle using the last interval (extrapolation)
+    if paired:
+        last_t, last_d = paired[-1]
+        second_last_t = paired[-2][0] if len(paired) >= 2 else 0.0
+        extra_ttm = last_t - second_last_t
+        cycles.append(
+            {
+                "fh_threshold": last_t + extra_ttm,
+                "ttm": extra_ttm,
+                "duration_hours": last_d * 24.0,
+            }
+        )
+
+    return cycles
+
+
+def build_fleet(
+    aircraft_df: pd.DataFrame,
+    checks_df: pd.DataFrame,
+    planning_start: datetime,
+) -> pd.DataFrame:
+    """
+    Combine aircraft and checks tables to produce the fleet reference DataFrame.
+
+    Columns: tail_number, model, fh_total, checks (list of cycle dicts),
+             ttm_hours (first upcoming TTM), available_from.
+    """
+    thresholds = checks_df["fh_threshold"].astype(float).tolist()
+    durations = checks_df["duration_days"].astype(float).tolist()
+
+    records = []
+    for _, row in aircraft_df.iterrows():
+        fh = float(row["fh_total"])
+        cycles = _check_cycles(fh, thresholds, durations)
+        records.append(
+            {
+                "tail_number": str(row["tail_number"]).strip(),
+                "model": str(row.get("model", "")).strip(),
+                "fh_total": fh,
+                "checks": cycles,
+                "ttm_hours": cycles[0]["ttm"] if cycles else 0.0,
+                "available_from": planning_start,
+            }
+        )
+
+    return pd.DataFrame(records)
+
+
+# ── Public entry points ───────────────────────────────────────────────────────
+
+def load_from_dfs(dfs: Dict[str, pd.DataFrame], cfg: RoutingConfig) -> Dict[str, pd.DataFrame]:
+    """
+    Same processing as load_all but accepts pre-loaded DataFrames.
+
+    dfs keys: 'aircraft', 'checks', 'airports', 'schedule'
+    The DataFrames may use the original CSV column names (synonym mapping is applied).
+    'schedule' must already have columns: DATA, ETAPA, DEP, ARR, HORA_DEP,
+    HORA_ARR, TEMPO_VOO, SEGMTO, MISSAO, OFRAG.
+    """
+    from .inspect_files import _map_columns, _AIRCRAFT_SYNONYMS, _CHECK_SYNONYMS, _AIRPORT_SYNONYMS
+
+    def _norm(df: pd.DataFrame, synonyms: Dict) -> pd.DataFrame:
+        df = df.copy().dropna(how="all")
+        mapping = _map_columns(list(df.columns), synonyms)
+        return df.rename(columns={v: k for k, v in mapping.items()})
+
+    aircraft_df = _norm(dfs["aircraft"], _AIRCRAFT_SYNONYMS)
+    checks_df = _norm(dfs["checks"], _CHECK_SYNONYMS)
+    airports_df = _norm(dfs["airports"], _AIRPORT_SYNONYMS)
+    schedule_raw = dfs["schedule"].copy()
+
+    # Drop obviously empty rows
+    for col in ("tail_number",):
+        if col in aircraft_df.columns:
+            aircraft_df = aircraft_df.dropna(subset=[col])
+    for col in ("fh_threshold",):
+        if col in checks_df.columns:
+            checks_df = checks_df.dropna(subset=[col])
+
+    # Maintenance bases – accept "1", "True", "true", 1
+    base_col = airports_df.get("is_maintenance_base", pd.Series(dtype=object))
+    base_mask = base_col.astype(str).str.strip().isin(["1", "True", "true"])
+    base_codes = airports_df.loc[base_mask, "airport_code"].str.upper().tolist() if "airport_code" in airports_df.columns else []
+    if not base_codes:
+        base_codes = [cfg.maintenance_base_code]
+    logger.info("Maintenance base(s): %s", base_codes)
+
+    schedule_df = _parse_schedule_datetimes(schedule_raw, cfg.planning_year)
+    ofrags_all = build_ofrags(schedule_df, base_codes)
+    ofrags = ofrags_all[ofrags_all["starts_at_base"] & ofrags_all["ends_at_base"]].copy().reset_index(drop=True)
+    logger.info("OFRAGs after base filter: %d / %d", len(ofrags), len(ofrags_all))
+
+    first_dep = ofrags["departure"].min() if not ofrags.empty else datetime.now()
+    planning_start = first_dep.replace(hour=0, minute=0, second=0, microsecond=0)
+    fleet = build_fleet(aircraft_df, checks_df, planning_start)
+
+    return {"ofrags": ofrags, "fleet": fleet, "airports": airports_df, "schedule": schedule_df}
+
+
+def load_all(cfg: RoutingConfig) -> Dict[str, pd.DataFrame]:
+    """
+    Read all raw files and return a dict with keys:
+        'ofrags'  – OFRAG profiles (only those that start and end at the maintenance base)
+        'fleet'   – fleet reference with TTM cycles
+        'airports'– airport table
+    """
+    raw = cfg.raw_dir
+
+    aircraft_df = read_aircraft(raw / cfg.aircraft_file)
+    checks_df = read_checks(raw / cfg.checks_file)
+    airports_df = read_airports(raw / cfg.airports_file)
+    schedule_raw = read_schedule(raw / cfg.flight_schedule_file)
+
+    # Maintenance-base ICAO codes
+    base_mask = airports_df.get("is_maintenance_base", pd.Series(dtype=int)).astype(int) == 1
+    base_codes = airports_df.loc[base_mask, "airport_code"].str.upper().tolist()
+    if not base_codes:
+        base_codes = [cfg.maintenance_base_code]
+    logger.info("Maintenance base(s): %s", base_codes)
+
+    # Parse schedule
+    schedule_df = _parse_schedule_datetimes(schedule_raw, cfg.planning_year)
+
+    # Build OFRAG table
+    ofrags_all = build_ofrags(schedule_df, base_codes)
+    ofrags = ofrags_all[ofrags_all["starts_at_base"] & ofrags_all["ends_at_base"]].copy()
+    ofrags = ofrags.reset_index(drop=True)
+    logger.info("OFRAGs after base filter: %d / %d", len(ofrags), len(ofrags_all))
+
+    # Planning horizon start = midnight of the earliest OFRAG departure day
+    # (aircraft are available from start-of-day, not the exact departure time)
+    first_dep = ofrags["departure"].min() if not ofrags.empty else datetime.now()
+    planning_start = first_dep.replace(hour=0, minute=0, second=0, microsecond=0)
+
+    # Build fleet
+    fleet = build_fleet(aircraft_df, checks_df, planning_start)
+
+    return {"ofrags": ofrags, "fleet": fleet, "airports": airports_df, "schedule": schedule_df}

src/routing_engine/inspect_files.py

@@ -0,0 +1,160 @@
+"""Auto-inspection of raw input files: detect encodings, separators, and column mappings."""
+
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+from typing import Dict, List, Optional
+
+import pandas as pd
+
+logger = logging.getLogger(__name__)
+
+# ── Column-name synonym dictionaries ─────────────────────────────────────────
+
+_AIRCRAFT_SYNONYMS: Dict[str, List[str]] = {
+    "tail_number":  ["matricula", "tail", "registration", "aeronave", "ac"],
+    "model":        ["modelo", "model", "type", "tipo"],
+    "fh_total":     ["fh totais", "fh_total", "total fh", "horas totais", "flight hours"],
+}
+
+_CHECK_SYNONYMS: Dict[str, List[str]] = {
+    "check_name":        ["checks", "check", "nome", "name", "descricao"],
+    "fh_threshold":      ["fh", "threshold", "limite fh", "horas", "hours"],
+    "duration_days":     ["tempo de execucao", "duracao", "duration", "dias", "days"],
+    "location":          ["local de execucao", "local", "location", "base"],
+}
+
+_AIRPORT_SYNONYMS: Dict[str, List[str]] = {
+    "airport_code":      ["airport_code", "icao", "code", "codigo"],
+    "airport_name":      ["airport_name", "name", "nome"],
+    "is_maintenance_base": ["is_maintenance_base", "base", "manutencao"],
+}
+
+_SCHEDULE_COL_NAMES = [
+    "DATA", "ETAPA", "DEP", "ARR",
+    "HORA_DEP", "HORA_ARR", "TEMPO_VOO",
+    "SEGMTO", "MISSAO", "OFRAG",
+]
+
+
+def _normalise(text: str) -> str:
+    """Lowercase + strip accents (ASCII fold)."""
+    import unicodedata
+    nfkd = unicodedata.normalize("NFKD", str(text))
+    return "".join(c for c in nfkd if not unicodedata.combining(c)).lower().strip()
+
+
+def _detect_encoding(filepath: Path) -> str:
+    for enc in ("utf-8-sig", "utf-8", "latin-1", "cp1252"):
+        try:
+            with open(filepath, encoding=enc) as f:
+                f.read(2048)
+            return enc
+        except UnicodeDecodeError:
+            continue
+    return "latin-1"
+
+
+def _detect_separator(filepath: Path, encoding: str) -> str:
+    with open(filepath, encoding=encoding) as f:
+        sample = f.read(512)
+    counts = {sep: sample.count(sep) for sep in (";", ",", "\t", "|")}
+    return max(counts, key=counts.get)
+
+
+def _map_columns(df_columns: List[str], synonyms: Dict[str, List[str]]) -> Dict[str, str]:
+    """Return {canonical_name: actual_column_name} for columns that can be matched."""
+    mapping: Dict[str, str] = {}
+    norm_cols = {_normalise(c): c for c in df_columns}
+
+    for canonical, candidates in synonyms.items():
+        for cand in candidates:
+            if _normalise(cand) in norm_cols:
+                mapping[canonical] = norm_cols[_normalise(cand)]
+                break
+        if canonical not in mapping:
+            # Partial match fallback
+            for norm_col, orig_col in norm_cols.items():
+                if any(_normalise(cand) in norm_col for cand in candidates):
+                    mapping[canonical] = orig_col
+                    break
+
+    return mapping
+
+
+def inspect_file(filepath: Path) -> Dict:
+    """Return metadata dict for a single raw file."""
+    enc = _detect_encoding(filepath)
+    sep = _detect_separator(filepath, enc)
+    try:
+        df = pd.read_csv(filepath, sep=sep, encoding=enc, nrows=5)
+        return {
+            "path": str(filepath),
+            "encoding": enc,
+            "separator": sep,
+            "columns": list(df.columns),
+            "n_cols": len(df.columns),
+            "sample_rows": df.to_dict(orient="records"),
+        }
+    except Exception as exc:
+        logger.warning("Could not read %s: %s", filepath, exc)
+        return {"path": str(filepath), "error": str(exc)}
+
+
+def inspect_all(raw_dir: Path) -> Dict[str, Dict]:
+    """Inspect every CSV in raw_dir and return a metadata dict keyed by stem."""
+    results: Dict[str, Dict] = {}
+    for p in sorted(raw_dir.glob("*.csv")):
+        results[p.stem] = inspect_file(p)
+        logger.info("Inspected %s  (%d cols)", p.name, results[p.stem].get("n_cols", 0))
+    return results
+
+
+def read_aircraft(filepath: Path) -> pd.DataFrame:
+    enc = _detect_encoding(filepath)
+    sep = _detect_separator(filepath, enc)
+    df = pd.read_csv(filepath, sep=sep, encoding=enc)
+    mapping = _map_columns(list(df.columns), _AIRCRAFT_SYNONYMS)
+    logger.info("Aircraft column map: %s", mapping)
+    df = df.rename(columns={v: k for k, v in mapping.items()})
+    return df
+
+
+def read_checks(filepath: Path) -> pd.DataFrame:
+    enc = _detect_encoding(filepath)
+    sep = _detect_separator(filepath, enc)
+    df = pd.read_csv(filepath, sep=sep, encoding=enc)
+    mapping = _map_columns(list(df.columns), _CHECK_SYNONYMS)
+    logger.info("Checks column map: %s", mapping)
+    df = df.rename(columns={v: k for k, v in mapping.items()})
+    return df
+
+
+def read_airports(filepath: Path) -> pd.DataFrame:
+    enc = _detect_encoding(filepath)
+    sep = _detect_separator(filepath, enc)
+    df = pd.read_csv(filepath, sep=sep, encoding=enc)
+    mapping = _map_columns(list(df.columns), _AIRPORT_SYNONYMS)
+    logger.info("Airports column map: %s", mapping)
+    df = df.rename(columns={v: k for k, v in mapping.items()})
+    return df
+
+
+def read_schedule(filepath: Path) -> pd.DataFrame:
+    """Read the two-row merged-header flight schedule."""
+    enc = _detect_encoding(filepath)
+    sep = _detect_separator(filepath, enc)
+    df = pd.read_csv(
+        filepath,
+        sep=sep,
+        encoding=enc,
+        header=None,
+        skiprows=2,
+        names=_SCHEDULE_COL_NAMES,
+        dtype=str,
+    )
+    df = df.dropna(subset=["DATA", "OFRAG"])
+    df = df[df["DATA"].str.strip() != ""]
+    df = df[df["OFRAG"].str.strip() != ""]
+    return df.reset_index(drop=True)

src/routing_engine/maintenance_monitor.py

@@ -0,0 +1,173 @@
+"""
+Maintenance monitor: validate route TTM compliance and compute maintenance events.
+
+A Route is TTM-compliant if at every point in the route the accumulated flight
+hours since the last maintenance check do not exceed the current check cycle's TTM.
+Maintenance is forced when adding the next OFRAG would exceed that TTM.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta
+from typing import Dict, List, Optional, Tuple
+
+import pandas as pd
+
+from .config import RoutingConfig
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class MaintenanceEvent:
+    after_ofrag_id: Optional[str]   # None = before first OFRAG (proactive)
+    check_cycle_index: int           # which check cycle is being performed
+    fh_threshold: float
+    accum_fh_at_check: float         # accumulated FH at the moment of the check
+    ttm_loss: float                  # = cycle_ttm - accum_fh_at_check
+    calendar_start: datetime
+    calendar_end: datetime
+
+
+@dataclass
+class RouteValidationResult:
+    feasible: bool
+    total_flight_hours: float
+    total_ttm_loss: float
+    maintenance_events: List[MaintenanceEvent] = field(default_factory=list)
+    infeasibility_reason: str = ""
+
+
+def validate_route(
+    ofrag_sequence: List[str],
+    ofrags_df: pd.DataFrame,
+    aircraft_checks: List[Dict],   # ordered list of {'fh_threshold','ttm','duration_hours'}
+    aircraft_available_from: datetime,
+    cfg: RoutingConfig,
+) -> RouteValidationResult:
+    """
+    Simulate flying a route and return whether it is TTM-feasible.
+
+    aircraft_checks is the list produced by ingest._check_cycles(); it encodes
+    the sequence of upcoming check cycles for this specific aircraft.
+    """
+    ofrag_index = {row["ofrag_id"]: row for _, row in ofrags_df.iterrows()}
+    tat = timedelta(minutes=cfg.tat_minutes)
+
+    check_idx = 0           # which cycle we are currently in
+    accum_fh = 0.0          # FH accumulated in the current cycle
+    current_time = aircraft_available_from
+    total_fh = 0.0
+    total_loss = 0.0
+    events: List[MaintenanceEvent] = []
+
+    for ofrag_id in ofrag_sequence:
+        if ofrag_id not in ofrag_index:
+            return RouteValidationResult(
+                feasible=False, total_flight_hours=total_fh, total_ttm_loss=total_loss,
+                infeasibility_reason=f"OFRAG {ofrag_id} not found",
+            )
+
+        ofrag = ofrag_index[ofrag_id]
+        h = float(ofrag["flight_hours"])
+        dep = ofrag["departure"]
+        arr = ofrag["arrival"]
+
+        if check_idx >= len(aircraft_checks):
+            return RouteValidationResult(
+                feasible=False, total_flight_hours=total_fh, total_ttm_loss=total_loss,
+                infeasibility_reason="No remaining check cycles – route exceeds maintenance plan",
+            )
+
+        cycle = aircraft_checks[check_idx]
+
+        # Can the current cycle accommodate this OFRAG?
+        if accum_fh + h > cycle["ttm"]:
+            # Maintenance required before this OFRAG
+            dur = timedelta(hours=cycle["duration_hours"])
+            maint_end = current_time + dur
+
+            if maint_end + tat > dep:
+                return RouteValidationResult(
+                    feasible=False, total_flight_hours=total_fh, total_ttm_loss=total_loss,
+                    infeasibility_reason=(
+                        f"No time for maintenance before {ofrag_id}: "
+                        f"maint ends {maint_end}, OFRAG departs {dep}"
+                    ),
+                )
+
+            loss = cycle["ttm"] - accum_fh
+            ofrag_pos = ofrag_sequence.index(ofrag_id)
+            event = MaintenanceEvent(
+                after_ofrag_id=None if ofrag_pos == 0 else ofrag_sequence[ofrag_pos - 1],
+                check_cycle_index=check_idx,
+                fh_threshold=cycle["fh_threshold"],
+                accum_fh_at_check=accum_fh,
+                ttm_loss=loss,
+                calendar_start=current_time,
+                calendar_end=maint_end,
+            )
+            events.append(event)
+            total_loss += loss
+            accum_fh = 0.0
+            check_idx += 1
+            current_time = maint_end
+
+            # Verify new cycle can accommodate the OFRAG
+            if check_idx >= len(aircraft_checks):
+                return RouteValidationResult(
+                    feasible=False, total_flight_hours=total_fh, total_ttm_loss=total_loss,
+                    infeasibility_reason="No remaining check cycles after maintenance",
+                )
+            cycle = aircraft_checks[check_idx]
+            if h > cycle["ttm"]:
+                return RouteValidationResult(
+                    feasible=False, total_flight_hours=total_fh, total_ttm_loss=total_loss,
+                    infeasibility_reason=f"OFRAG {ofrag_id} ({h:.1f} FH) exceeds cycle TTM ({cycle['ttm']:.1f} FH)",
+                )
+
+        # Time feasibility: aircraft must be available before OFRAG departs
+        if current_time + tat > dep:
+            return RouteValidationResult(
+                feasible=False, total_flight_hours=total_fh, total_ttm_loss=total_loss,
+                infeasibility_reason=(
+                    f"Time conflict: aircraft ready at {current_time + tat}, "
+                    f"but {ofrag_id} departs at {dep}"
+                ),
+            )
+
+        accum_fh += h
+        total_fh += h
+        current_time = arr
+
+    return RouteValidationResult(
+        feasible=True,
+        total_flight_hours=total_fh,
+        total_ttm_loss=total_loss,
+        maintenance_events=events,
+    )
+
+
+def summarise_fleet_maintenance(solution_routes: List[Dict], ofrags_df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Build a human-readable maintenance summary for the complete solution.
+    Each row = one maintenance event.
+    """
+    rows = []
+    for r in solution_routes:
+        for evt in r.get("maintenance_events", []):
+            rows.append(
+                {
+                    "tail_number": r["aircraft_id"],
+                    "after_ofrag": evt.after_ofrag_id,
+                    "check_cycle_index": evt.check_cycle_index,
+                    "fh_threshold": evt.fh_threshold,
+                    "accum_fh_at_check": round(evt.accum_fh_at_check, 2),
+                    "ttm_loss_hours": round(evt.ttm_loss, 2),
+                    "maint_start": evt.calendar_start,
+                    "maint_end": evt.calendar_end,
+                }
+            )
+    return pd.DataFrame(rows)

src/routing_engine/metrics.py

@@ -0,0 +1,117 @@
+"""Compute and format output metrics from the optimiser solution."""
+
+from __future__ import annotations
+
+from typing import Dict, List
+
+import pandas as pd
+
+
+def build_schedule_table(routes: List[Dict], ofrags_df: pd.DataFrame) -> pd.DataFrame:
+    """One row per OFRAG in the solution, showing which aircraft serves it."""
+    ofrag_lookup = ofrags_df.set_index("ofrag_id")
+    rows = []
+    for r in routes:
+        prev_arr = r.get("aircraft_available_from", None)
+        for pos, oid in enumerate(r["ofrag_ids"]):
+            maint_before = pos in r["maint_before_index"]
+            ofrag_row = ofrag_lookup.loc[oid] if oid in ofrag_lookup.index else {}
+            rows.append(
+                {
+                    "aircraft": r["aircraft_id"],
+                    "position_in_route": pos + 1,
+                    "maintenance_before": maint_before,
+                    "ofrag_id": oid,
+                    "missions": ofrag_row.get("missions", ""),
+                    "departure": ofrag_row.get("departure", pd.NaT),
+                    "arrival": ofrag_row.get("arrival", pd.NaT),
+                    "flight_hours": ofrag_row.get("flight_hours", 0.0),
+                    "n_legs": ofrag_row.get("n_legs", 0),
+                }
+            )
+    return pd.DataFrame(rows).sort_values(["aircraft", "departure"]).reset_index(drop=True)
+
+
+def build_maintenance_table(routes: List[Dict]) -> pd.DataFrame:
+    """One row per maintenance event in the solution."""
+    rows = []
+    for r in routes:
+        for evt in r.get("maintenance_events", []):
+            rows.append(
+                {
+                    "aircraft": r["aircraft_id"],
+                    "after_ofrag": evt.after_ofrag_id,
+                    "check_cycle_index": evt.check_cycle_index,
+                    "fh_threshold": evt.fh_threshold,
+                    "accum_fh_at_check": round(evt.accum_fh_at_check, 2),
+                    "ttm_loss_hours": round(evt.ttm_loss, 2),
+                    "maint_start": evt.calendar_start,
+                    "maint_end": evt.calendar_end,
+                }
+            )
+    return pd.DataFrame(rows)
+
+
+def build_fleet_summary(routes: List[Dict], fleet_df: pd.DataFrame) -> pd.DataFrame:
+    """One row per aircraft with utilisation statistics."""
+    utilisation: Dict[str, Dict] = {
+        aid: {
+            "flight_hours": 0.0,
+            "n_ofrags": 0,
+            "n_maint_events": 0,
+            "total_ttm_loss": 0.0,
+        }
+        for aid in fleet_df["tail_number"].tolist()
+    }
+
+    for r in routes:
+        aid = r["aircraft_id"]
+        if aid in utilisation:
+            utilisation[aid]["flight_hours"] += r["flight_hours"]
+            utilisation[aid]["n_ofrags"] += len(r["ofrag_ids"])
+            utilisation[aid]["n_maint_events"] += len(r.get("maintenance_events", []))
+            utilisation[aid]["total_ttm_loss"] += r["ttm_loss"]
+
+    rows = []
+    for _, ac in fleet_df.iterrows():
+        aid = ac["tail_number"]
+        u = utilisation.get(aid, {})
+        ttm0 = ac["ttm_hours"]
+        fh_done = u.get("flight_hours", 0.0)
+        rows.append(
+            {
+                "aircraft": aid,
+                "model": ac.get("model", ""),
+                "initial_ttm_h": round(ttm0, 2),
+                "flight_hours_scheduled": round(fh_done, 2),
+                "ttm_utilisation_pct": round(fh_done / ttm0 * 100, 1) if ttm0 > 0 else 0.0,
+                "n_ofrags_assigned": u.get("n_ofrags", 0),
+                "n_maintenance_events": u.get("n_maint_events", 0),
+                "total_ttm_loss_h": round(u.get("total_ttm_loss", 0.0), 2),
+            }
+        )
+
+    df = pd.DataFrame(rows)
+    df["idle"] = df["n_ofrags_assigned"] == 0
+    return df
+
+
+def solution_summary(result: Dict, ofrags_df: pd.DataFrame, fleet_df: pd.DataFrame) -> Dict:
+    """Return a compact summary dict of the optimisation result."""
+    routes = result.get("routes", [])
+    total_ofrags = len(ofrags_df)
+    covered = sum(len(r["ofrag_ids"]) for r in routes)
+    uncovered = result.get("uncovered_ofrags", [])
+    total_ttm_loss = sum(r["ttm_loss"] for r in routes)
+    n_maint = sum(len(r.get("maintenance_events", [])) for r in routes)
+
+    return {
+        "status": result.get("status", "?"),
+        "objective": round(result.get("objective", 0.0), 4),
+        "total_ofrags": total_ofrags,
+        "covered_ofrags": covered,
+        "uncovered_ofrags": uncovered,
+        "total_ttm_loss_hours": round(total_ttm_loss, 2),
+        "n_maintenance_events": n_maint,
+        "columns_generated": result.get("total_columns_generated", 0),
+    }

src/routing_engine/network_generator.py

@@ -0,0 +1,84 @@
+"""
+Time-space network for the OFRAG routing problem.
+
+Builds the precedence / adjacency structure used by the pricing subproblem:
+  - can_follow[i][j]             : OFRAG j can start directly after OFRAG i (no maintenance)
+  - can_follow_with_check[k][i][j]: OFRAG j can start after OFRAG i + check-cycle k maintenance
+"""
+
+from __future__ import annotations
+
+import logging
+from datetime import timedelta
+from typing import Dict, List
+
+import pandas as pd
+
+from .config import RoutingConfig
+
+logger = logging.getLogger(__name__)
+
+
+def build_adjacency(
+    ofrags: pd.DataFrame,
+    checks_unique_durations: List[float],
+    cfg: RoutingConfig,
+) -> Dict:
+    """
+    Pre-compute adjacency matrices.
+
+    Parameters
+    ----------
+    ofrags : sorted by departure, indexed 0..n-1
+    checks_unique_durations : list of check-cycle durations in hours (one per cycle)
+    cfg : RoutingConfig
+
+    Returns
+    -------
+    dict with keys:
+        'can_follow'            : List[List[bool]]  (n × n)
+        'can_follow_with_check' : List[List[List[bool]]]  (n_checks × n × n)
+        'n_ofrags'              : int
+        'n_checks'              : int
+    """
+    n = len(ofrags)
+    tat = timedelta(minutes=cfg.tat_minutes)
+
+    arrivals = ofrags["arrival"].tolist()
+    departures = ofrags["departure"].tolist()
+
+    # Direct adjacency (no maintenance between i and j)
+    can_follow = [[False] * n for _ in range(n)]
+    for i in range(n):
+        for j in range(n):
+            if i != j and arrivals[i] + tat <= departures[j]:
+                can_follow[i][j] = True
+
+    # Adjacency after check cycle k (maintenance inserted between i and j)
+    n_checks = len(checks_unique_durations)
+    can_follow_with_check = [
+        [[False] * n for _ in range(n)] for _ in range(n_checks)
+    ]
+    for k, dur_hours in enumerate(checks_unique_durations):
+        dur = timedelta(hours=dur_hours)
+        for i in range(n):
+            for j in range(n):
+                if i != j and arrivals[i] + dur + tat <= departures[j]:
+                    can_follow_with_check[k][i][j] = True
+
+    stats = {
+        "direct_edges": sum(sum(row) for row in can_follow),
+        "check_edges_per_cycle": [
+            sum(sum(row) for row in can_follow_with_check[k])
+            for k in range(n_checks)
+        ],
+    }
+    logger.info("Network  n_ofrags=%d  direct_edges=%d", n, stats["direct_edges"])
+
+    return {
+        "can_follow": can_follow,
+        "can_follow_with_check": can_follow_with_check,
+        "n_ofrags": n,
+        "n_checks": n_checks,
+        "stats": stats,
+    }

src/routing_engine/optimizer.py

@@ -0,0 +1,550 @@
+"""
+Aircraft Routing Optimizer
+==========================
+Solves the Aircraft Routing problem via:
+
+  1. Column Generation (CG)  – builds the LP relaxation of the Set Partitioning
+     formulation iteratively by pricing new columns with a label-setting DP.
+  2. Branch and Bound (B&B)  – applied by PuLP/CBC on the full column pool once
+     CG has converged, producing the optimal integer schedule.
+
+Mathematical formulation
+------------------------
+  Minimise   Σ_r  c_r · x_r                         (total TTM loss)
+  s.t.       Σ_{r: j∈r}  x_r  = 1   ∀ j ∈ OFRAGs  (each OFRAG covered once)
+             Σ_{r: a(r)=a} x_r ≤ 1  ∀ a ∈ Aircraft  (one route per aircraft)
+             x_r ∈ {0, 1}
+
+Column-generation pricing subproblem
+-------------------------------------
+For each aircraft a, find the feasible route r* that minimises:
+
+    c_r  –  Σ_{j ∈ r} π_j  –  μ_a
+
+where π_j = dual variable of coverage constraint for OFRAG j,
+      μ_a = dual variable of aircraft-usage constraint for aircraft a.
+
+This is solved by label-setting DP on the DAG of OFRAGs ordered by departure.
+
+Label at (node j, check_cycle k): (reduced_cost, accum_fh, route_trace)
+  - reduced_cost : running objective (negative → route is profitable to add)
+  - accum_fh     : flight hours accumulated since last maintenance
+  - route_trace  : list of (ofrag_idx, maint_before_flag)
+
+Dominance rule  (for labels at the same node and same check_cycle k)
+---------------------------------------------------------------------
+Label 1 (rc1, h1) dominates Label 2 (rc2, h2) iff:
+    rc1 ≤ rc2   AND   (h1 – rc1) ≥ (h2 – rc2)
+
+This ensures Label 1 is never worse than Label 2 on any future extension,
+whether that extension requires maintenance or not.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+from datetime import timedelta
+from typing import Dict, List, Optional, Tuple
+
+import pandas as pd
+import pulp
+
+from .config import RoutingConfig
+from .maintenance_monitor import validate_route, MaintenanceEvent
+
+logger = logging.getLogger(__name__)
+
+_EPS = 1e-9   # float comparison tolerance
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Data structures
+# ─────────────────────────────────────────────────────────────────────────────
+
+@dataclass
+class Route:
+    """A feasible schedule for one aircraft (= one column in the master problem)."""
+    route_id: int
+    aircraft_id: str
+    ofrag_ids: List[str]              # OFRAGs served, in order
+    maint_before_index: List[int]     # positions in ofrag_ids where maint precedes
+    flight_hours: float
+    ttm_loss: float                   # objective cost
+    coverage: frozenset               # frozenset of ofrag_ids covered
+
+    @property
+    def cost(self) -> float:
+        return self.ttm_loss
+
+    def reduced_cost(self, dual_ofrags: Dict[str, float], dual_aircraft: float) -> float:
+        return self.ttm_loss - sum(dual_ofrags.get(j, 0.0) for j in self.ofrag_ids) - dual_aircraft
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Helpers
+# ─────────────────────────────────────────────────────────────────────────────
+
+def _prune_labels(
+    labels: List[Tuple[float, float, float, List]],
+) -> List[Tuple[float, float, float, List]]:
+    """
+    Remove dominated labels.  Each label is (rc, h, cost, trace).
+      rc   = running reduced cost (includes dual subtractions)
+      h    = accumulated FH since last maintenance event
+      cost = TTM loss accumulated so far (no dual subtractions)
+      trace = list of (ofrag_idx, maint_before_flag)
+
+    Dominance: Label 1 dominates Label 2 iff rc1 ≤ rc2 AND (h1 - rc1) ≥ (h2 - rc2).
+    Pareto front: sorted by rc asc, keep only those with strictly increasing (h - rc).
+    """
+    if len(labels) <= 1:
+        return labels
+    labels_sorted = sorted(labels, key=lambda t: t[0])
+    pareto: List[Tuple[float, float, float, List]] = []
+    best_value = -float("inf")
+    for rc, h, cost, trace in labels_sorted:
+        v = h - rc
+        if v > best_value - _EPS:
+            pareto.append((rc, h, cost, trace))
+            best_value = max(best_value, v)
+    return pareto
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Main optimizer
+# ─────────────────────────────────────────────────────────────────────────────
+
+class AircraftRoutingOptimizer:
+    """
+    Orchestrates Column Generation + Branch and Bound.
+
+    Parameters
+    ----------
+    ofrags_df : DataFrame with columns [ofrag_id, departure, arrival, flight_hours]
+    fleet_df  : DataFrame with columns [tail_number, fh_total, checks, ttm_hours, available_from]
+                  where checks = list of {'fh_threshold','ttm','duration_hours'}
+    adjacency : dict returned by network_generator.build_adjacency()
+    cfg       : RoutingConfig
+    """
+
+    def __init__(
+        self,
+        ofrags_df: pd.DataFrame,
+        fleet_df: pd.DataFrame,
+        adjacency: Dict,
+        cfg: RoutingConfig,
+    ):
+        self.ofrags = ofrags_df.sort_values("departure").reset_index(drop=True)
+        self.fleet = fleet_df.reset_index(drop=True)
+        self.adj = adjacency
+        self.cfg = cfg
+
+        self._ofrag_ids: List[str] = self.ofrags["ofrag_id"].tolist()
+        self._ofrag_fh: Dict[str, float] = dict(
+            zip(self.ofrags["ofrag_id"], self.ofrags["flight_hours"])
+        )
+        self._ofrag_idx: Dict[str, int] = {oid: i for i, oid in enumerate(self._ofrag_ids)}
+        self._aircraft_ids: List[str] = self.fleet["tail_number"].tolist()
+
+        self._columns: List[Route] = []
+        self._route_counter = 0
+
+    # ── Public API ────────────────────────────────────────────────────────────
+
+    def solve(self) -> Dict:
+        """Run Column Generation → B&B and return the solution dict."""
+        logger.info("=== Aircraft Routing Optimizer ===")
+        logger.info("OFRAGs: %d   Aircraft: %d", len(self._ofrag_ids), len(self._aircraft_ids))
+
+        self._initialise_columns()
+        logger.info("Initial column pool: %d", len(self._columns))
+
+        self._column_generation()
+        logger.info("After CG: %d columns", len(self._columns))
+
+        result = self._solve_mip()
+
+        # Post-process: attach maintenance events to selected routes
+        result["routes"] = self._attach_maintenance_events(result["selected_routes"])
+        return result
+
+    # ── Initialisation ────────────────────────────────────────────────────────
+
+    def _next_id(self) -> int:
+        self._route_counter += 1
+        return self._route_counter
+
+    def _make_route(
+        self,
+        aircraft_id: str,
+        ofrag_ids: List[str],
+        maint_before_index: List[int],
+        aircraft_checks: List[Dict],
+        starting_check_idx: int = 0,
+    ) -> Route:
+        """Build a Route object from its components and compute its cost."""
+        fh_total = sum(self._ofrag_fh[o] for o in ofrag_ids)
+
+        # Compute TTM loss: for each maintenance event, loss = cycle.ttm - accumulated_at_that_point
+        ttm_loss = 0.0
+        check_idx = starting_check_idx
+        accum = 0.0
+        for pos, oid in enumerate(ofrag_ids):
+            if pos in maint_before_index and check_idx < len(aircraft_checks):
+                ttm_loss += aircraft_checks[check_idx]["ttm"] - accum
+                accum = 0.0
+                check_idx += 1
+            accum += self._ofrag_fh[oid]
+
+        return Route(
+            route_id=self._next_id(),
+            aircraft_id=aircraft_id,
+            ofrag_ids=ofrag_ids,
+            maint_before_index=maint_before_index,
+            flight_hours=fh_total,
+            ttm_loss=round(ttm_loss, 6),
+            coverage=frozenset(ofrag_ids),
+        )
+
+    def _initialise_columns(self):
+        """Seed the column pool with one single-OFRAG route per aircraft × OFRAG pair."""
+        tat = timedelta(minutes=self.cfg.tat_minutes)
+        n = len(self._ofrag_ids)
+
+        for _, ac in self.fleet.iterrows():
+            checks = ac["checks"]
+            avail = ac["available_from"]
+            ttm0 = checks[0]["ttm"] if checks else 0.0
+
+            added = False
+            for i in range(n):
+                dep = self.ofrags.iloc[i]["departure"]
+                fh = self.ofrags.iloc[i]["flight_hours"]
+                if avail + tat <= dep and fh <= ttm0:
+                    route = self._make_route(
+                        ac["tail_number"], [self._ofrag_ids[i]], [], checks
+                    )
+                    self._columns.append(route)
+                    added = True
+                    break  # one seed per aircraft is enough
+
+            if not added:
+                # Aircraft needs maintenance before first OFRAG; try with maint flag
+                for i in range(n):
+                    fh = self.ofrags.iloc[i]["flight_hours"]
+                    if len(checks) > 1 and fh <= checks[1]["ttm"]:
+                        route = self._make_route(
+                            ac["tail_number"], [self._ofrag_ids[i]], [0], checks
+                        )
+                        self._columns.append(route)
+                        break
+
+    # ── Column Generation ─────────────────────────────────────────────────────
+
+    def _column_generation(self):
+        for iteration in range(self.cfg.max_cg_iterations):
+            lp = self._solve_rmp_lp()
+            if lp is None:
+                logger.warning("RMP infeasible at iteration %d – stopping CG", iteration)
+                break
+
+            pi = lp["dual_ofrags"]     # coverage duals
+            mu = lp["dual_aircraft"]   # aircraft-usage duals
+
+            added_any = False
+            for _, ac in self.fleet.iterrows():
+                new_routes = self._price(ac, pi, mu.get(ac["tail_number"], 0.0))
+                for r in new_routes:
+                    self._columns.append(r)
+                    added_any = True
+
+            logger.debug(
+                "CG iter %d  obj=%.4f  cols=%d  added=%s",
+                iteration, lp["objective"], len(self._columns), added_any,
+            )
+
+            if not added_any:
+                logger.info("CG converged at iteration %d", iteration)
+                break
+
+    def _solve_rmp_lp(self) -> Optional[Dict]:
+        """Solve the LP relaxation of the Restricted Master Problem."""
+        prob = pulp.LpProblem("RMP", pulp.LpMinimize)
+
+        x = {
+            col.route_id: pulp.LpVariable(f"x{col.route_id}", lowBound=0, upBound=1)
+            for col in self._columns
+        }
+        # Artificial slack for coverage (ensures LP feasibility)
+        y = {
+            oid: pulp.LpVariable(f"y_{oid}", lowBound=0)
+            for oid in self._ofrag_ids
+        }
+
+        prob += (
+            pulp.lpSum(col.cost * x[col.route_id] for col in self._columns)
+            + pulp.lpSum(self.cfg.big_m * y[oid] for oid in self._ofrag_ids)
+        )
+
+        for oid in self._ofrag_ids:
+            relevant = [x[c.route_id] for c in self._columns if oid in c.coverage]
+            prob += pulp.lpSum(relevant) + y[oid] == 1, f"cov_{oid}"
+
+        for aid in self._aircraft_ids:
+            relevant = [x[c.route_id] for c in self._columns if c.aircraft_id == aid]
+            prob += pulp.lpSum(relevant) <= 1, f"ac_{aid}"
+
+        prob.solve(pulp.PULP_CBC_CMD(msg=0))
+
+        if pulp.LpStatus[prob.status] not in ("Optimal",):
+            return None
+
+        # Standard LP dual (shadow price): positive for coverage constraints when
+        # covered by artificials (≈ big_M), negative/zero for aircraft-usage constraints.
+        # Pricing reduced cost = c_r - Σ π_j - μ_a  uses these directly.
+        dual_ofrags = {}
+        for oid in self._ofrag_ids:
+            c = prob.constraints.get(f"cov_{oid}")
+            dual_ofrags[oid] = c.pi if (c and c.pi is not None) else 0.0
+
+        dual_aircraft = {}
+        for aid in self._aircraft_ids:
+            c = prob.constraints.get(f"ac_{aid}")
+            dual_aircraft[aid] = c.pi if (c and c.pi is not None) else 0.0
+
+        return {
+            "objective": pulp.value(prob.objective),
+            "dual_ofrags": dual_ofrags,
+            "dual_aircraft": dual_aircraft,
+        }
+
+    # ── Pricing subproblem (label-setting DP) ─────────────────────────────────
+
+    def _price(
+        self,
+        aircraft: pd.Series,
+        dual_ofrags: Dict[str, float],
+        dual_aircraft: float,
+    ) -> List[Route]:
+        """
+        Find all routes for *aircraft* with negative reduced cost.
+
+        State: (ofrag_idx j, check_cycle k)
+        Label: (rc, h, cost, trace)
+          rc    = running reduced cost (cost minus dual contributions so far)
+          h     = accumulated FH since last maintenance in trace
+          cost  = TTM loss accumulated so far (rc + duals; stored separately to
+                  avoid re-deriving cost at harvest time)
+          trace = list of (ofrag_idx, maint_before_flag)
+        """
+        checks = aircraft["checks"]
+        avail = aircraft["available_from"]
+        aid = aircraft["tail_number"]
+        tat = timedelta(minutes=self.cfg.tat_minutes)
+        n = len(self._ofrag_ids)
+        n_checks = len(checks)
+
+        can_follow = self.adj["can_follow"]
+        can_with_check = self.adj["can_follow_with_check"]
+
+        # labels[j][k] = list of (rc, h, cost, trace)
+        labels: List[List[List]] = [[[] for _ in range(n_checks)] for _ in range(n)]
+
+        # --- Seed: single-OFRAG routes starting from SOURCE ---
+        # For cycle k=0: aircraft starts directly.
+        # For cycle k>0: aircraft must complete checks[0..k-1] before OFRAG j departs,
+        #   each with 0 accumulated FH (no prior OFRAGs) → full TTM loss per empty check.
+        #   The prior_loss IS the route cost so far; it is tracked in both rc and cost.
+        for j in range(n):
+            dep_j = self.ofrags.iloc[j]["departure"]
+            fh_j = self.ofrags.iloc[j]["flight_hours"]
+            oid_j = self._ofrag_ids[j]
+
+            earliest = avail      # earliest calendar time to enter the current cycle
+            prior_loss = 0.0      # TTM loss from empty prior checks (= route cost so far)
+
+            for k in range(n_checks):
+                can_reach = earliest + tat <= dep_j
+                fits_ttm  = fh_j <= checks[k]["ttm"] + _EPS
+
+                if can_reach and fits_ttm:
+                    rc = prior_loss - dual_ofrags.get(oid_j, 0.0)
+                    cost = prior_loss  # TTM loss from prior empty checks; no in-trace maint yet
+                    labels[j][k].append((rc, fh_j, cost, [(j, False)]))
+                    break  # lowest feasible cycle with valid timing
+
+                if not can_reach:
+                    break  # no point advancing to later cycles (time only grows)
+
+                # Advance to next cycle: empty check k before OFRAG j
+                if k + 1 < n_checks:
+                    prior_loss += checks[k]["ttm"]
+                    earliest = earliest + timedelta(hours=checks[k]["duration_hours"])
+
+        # --- Forward expansion ---
+        new_routes: List[Route] = []
+
+        for j in range(n):
+            for k in range(n_checks):
+                labels[j][k] = _prune_labels(labels[j][k])
+
+                for rc, h, cost, trace in labels[j][k]:
+                    # Harvest: if this partial route has negative reduced cost, record it
+                    final_rc = rc - dual_aircraft
+                    if final_rc < -self.cfg.cg_tolerance:
+                        new_routes.append(
+                            self._trace_to_route(aid, trace, cost, checks)
+                        )
+
+                    # Extend to OFRAG m
+                    for m in range(j + 1, n):
+                        fh_m = self.ofrags.iloc[m]["flight_hours"]
+                        oid_m = self._ofrag_ids[m]
+                        gain = dual_ofrags.get(oid_m, 0.0)
+
+                        # Option A: direct (no maintenance)
+                        if can_follow[j][m] and h + fh_m <= checks[k]["ttm"] + _EPS:
+                            new_rc = rc - gain
+                            new_h = h + fh_m
+                            new_cost = cost  # no new maintenance event
+                            new_trace = trace + [(m, False)]
+                            labels[m][k].append((new_rc, new_h, new_cost, new_trace))
+
+                        # Option B: maintenance between j and m (cycle k → k+1)
+                        if k + 1 < n_checks:
+                            can_m = can_with_check[k][j][m] if k < len(can_with_check) else False
+                            if can_m and fh_m <= checks[k + 1]["ttm"] + _EPS:
+                                loss = checks[k]["ttm"] - h
+                                new_rc = rc + loss - gain
+                                new_h = fh_m
+                                new_cost = cost + loss  # add maintenance loss
+                                new_trace = trace + [(m, True)]   # True = maint before m
+                                labels[m][k + 1].append((new_rc, new_h, new_cost, new_trace))
+
+        # De-duplicate by coverage set (keep cheapest)
+        seen: Dict[frozenset, Route] = {}
+        for r in new_routes:
+            key = r.coverage
+            if key not in seen or r.ttm_loss < seen[key].ttm_loss:
+                seen[key] = r
+
+        return list(seen.values())
+
+    def _trace_to_route(
+        self,
+        aircraft_id: str,
+        trace: List[Tuple[int, bool]],
+        cost: float,
+        checks: List[Dict],
+    ) -> Route:
+        """
+        Convert a DP trace into a Route object using the pre-computed cost.
+
+        The cost stored in the label already accounts for:
+          - TTM losses from any prior empty checks done before the first OFRAG
+            (seed at cycle k>0)
+          - TTM losses from maintenance events within the trace (Option B expansions)
+        We must NOT re-derive cost from maint_before_index because prior empty checks
+        are not represented in the trace entries.
+        """
+        ofrag_ids = [self._ofrag_ids[idx] for idx, _ in trace]
+        maint_before_index = [pos for pos, (_, mb) in enumerate(trace) if mb]
+        fh_total = sum(self._ofrag_fh[oid] for oid in ofrag_ids)
+        return Route(
+            route_id=self._next_id(),
+            aircraft_id=aircraft_id,
+            ofrag_ids=ofrag_ids,
+            maint_before_index=maint_before_index,
+            flight_hours=fh_total,
+            ttm_loss=round(cost, 6),
+            coverage=frozenset(ofrag_ids),
+        )
+
+    # ── MIP (Branch and Bound) ────────────────────────────────────────────────
+
+    def _solve_mip(self) -> Dict:
+        """Solve the integer Set Partitioning model using PuLP/CBC B&B."""
+        prob = pulp.LpProblem("AircraftRouting_MIP", pulp.LpMinimize)
+
+        x = {
+            col.route_id: pulp.LpVariable(f"x{col.route_id}", cat="Binary")
+            for col in self._columns
+        }
+        y = {
+            oid: pulp.LpVariable(f"y_{oid}", lowBound=0)
+            for oid in self._ofrag_ids
+        }
+
+        prob += (
+            pulp.lpSum(col.cost * x[col.route_id] for col in self._columns)
+            + pulp.lpSum(self.cfg.big_m * y[oid] for oid in self._ofrag_ids)
+        )
+
+        for oid in self._ofrag_ids:
+            relevant = [x[c.route_id] for c in self._columns if oid in c.coverage]
+            prob += pulp.lpSum(relevant) + y[oid] == 1, f"cov_{oid}"
+
+        for aid in self._aircraft_ids:
+            relevant = [x[c.route_id] for c in self._columns if c.aircraft_id == aid]
+            prob += pulp.lpSum(relevant) <= 1, f"ac_{aid}"
+
+        solver = pulp.PULP_CBC_CMD(
+            msg=1,
+            timeLimit=self.cfg.mip_time_limit_seconds,
+            gapRel=self.cfg.mip_gap,
+        )
+        prob.solve(solver)
+
+        status = pulp.LpStatus[prob.status]
+        obj = pulp.value(prob.objective) or 0.0
+
+        selected = [
+            col for col in self._columns
+            if (x[col.route_id].varValue or 0) > 0.5
+        ]
+        uncovered = [
+            oid for oid in self._ofrag_ids
+            if (y[oid].varValue or 0) > 0.5
+        ]
+
+        logger.info("MIP status: %s  objective: %.4f", status, obj)
+        logger.info("Selected routes: %d  Uncovered OFRAGs: %d", len(selected), len(uncovered))
+
+        return {
+            "status": status,
+            "objective": obj,
+            "selected_routes": selected,
+            "uncovered_ofrags": uncovered,
+            "total_columns_generated": len(self._columns),
+        }
+
+    # ── Post-processing ───────────────────────────────────────────────────────
+
+    def _attach_maintenance_events(self, selected_routes: List[Route]) -> List[Dict]:
+        """
+        Run the maintenance monitor on each selected route to get full event detail.
+        Returns a list of dicts (one per route) ready for output / dashboard.
+        """
+        out = []
+        for route in selected_routes:
+            ac_row = self.fleet[self.fleet["tail_number"] == route.aircraft_id].iloc[0]
+            val = validate_route(
+                ofrag_sequence=route.ofrag_ids,
+                ofrags_df=self.ofrags,
+                aircraft_checks=ac_row["checks"],
+                aircraft_available_from=ac_row["available_from"],
+                cfg=self.cfg,
+            )
+            out.append(
+                {
+                    "aircraft_id": route.aircraft_id,
+                    "ofrag_ids": route.ofrag_ids,
+                    "maint_before_index": route.maint_before_index,
+                    "flight_hours": route.flight_hours,
+                    "ttm_loss": route.ttm_loss,
+                    "feasible": val.feasible,
+                    "maintenance_events": val.maintenance_events,
+                }
+            )
+        return out

src/routing_engine/pipeline.py

@@ -0,0 +1,124 @@
+"""
+End-to-end orchestration pipeline.
+
+Usage
+-----
+    from src.routing_engine import RoutingPipeline, DEFAULT_CONFIG
+    pipe = RoutingPipeline(DEFAULT_CONFIG)
+    result = pipe.run()
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import sys
+from pathlib import Path
+from typing import Dict, Optional
+
+import pandas as pd
+
+from .config import RoutingConfig, DEFAULT_CONFIG
+from .ingest import load_all, load_from_dfs
+from .network_generator import build_adjacency
+from .optimizer import AircraftRoutingOptimizer
+from .quality import run_all as quality_check
+from .metrics import (
+    build_schedule_table,
+    build_maintenance_table,
+    build_fleet_summary,
+    solution_summary,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _setup_logging(level: str):
+    logging.basicConfig(
+        level=getattr(logging, level.upper(), logging.INFO),
+        format="%(asctime)s  %(levelname)-8s  %(name)s  %(message)s",
+        datefmt="%H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+
+
+class RoutingPipeline:
+    def __init__(self, cfg: RoutingConfig = DEFAULT_CONFIG):
+        self.cfg = cfg
+        _setup_logging(cfg.log_level)
+
+    def run(self, save_outputs: bool = True, raw_dfs=None) -> Dict:
+        """Execute all pipeline stages and return a results dict."""
+        logger.info("-- Stage 1: Ingest --------------------------------------")
+        data = load_from_dfs(raw_dfs, self.cfg) if raw_dfs is not None else load_all(self.cfg)
+        ofrags = data["ofrags"]
+        fleet = data["fleet"]
+
+        logger.info("-- Stage 2: Quality check -------------------------------")
+        qc = quality_check(ofrags, fleet)
+        if not qc["ok"]:
+            logger.warning("Quality issues detected:\n  %s", "\n  ".join(qc["issues"]))
+
+        logger.info("-- Stage 3: Build network -------------------------------")
+        # Collect unique check-cycle durations (for adjacency-with-check matrices)
+        all_durations = set()
+        for _, ac in fleet.iterrows():
+            for c in ac["checks"]:
+                all_durations.add(c["duration_hours"])
+        sorted_durations = sorted(all_durations)
+
+        adj = build_adjacency(ofrags, sorted_durations, self.cfg)
+
+        logger.info("-- Stage 4: Optimise ------------------------------------")
+        opt = AircraftRoutingOptimizer(ofrags, fleet, adj, self.cfg)
+        result = opt.solve()
+
+        logger.info("-- Stage 5: Metrics -------------------------------------")
+        routes = result.get("routes", [])
+        schedule_df = build_schedule_table(routes, ofrags)
+        maint_df = build_maintenance_table(routes)
+        fleet_df = build_fleet_summary(routes, fleet)
+        summary = solution_summary(result, ofrags, fleet)
+
+        logger.info(
+            "\n%s",
+            "\n".join(f"  {k}: {v}" for k, v in summary.items()),
+        )
+
+        if save_outputs:
+            self._save(schedule_df, maint_df, fleet_df, summary)
+
+        return {
+            "summary": summary,
+            "schedule": schedule_df,
+            "maintenance": maint_df,
+            "fleet": fleet_df,
+            "ofrags": ofrags,
+            "raw_result": result,
+            "quality": qc,
+        }
+
+    def _save(
+        self,
+        schedule_df: pd.DataFrame,
+        maint_df: pd.DataFrame,
+        fleet_df: pd.DataFrame,
+        summary: Dict,
+    ):
+        cfg = self.cfg
+        cfg.schedules_dir.mkdir(parents=True, exist_ok=True)
+        cfg.exports_dir.mkdir(parents=True, exist_ok=True)
+        cfg.processed_dir.mkdir(parents=True, exist_ok=True)
+
+        schedule_df.to_csv(cfg.schedules_dir / "flight_strings.csv", index=False)
+        maint_df.to_csv(cfg.schedules_dir / "maintenance_events.csv", index=False)
+        fleet_df.to_csv(cfg.exports_dir / "fleet_utilisation.csv", index=False)
+
+        with open(cfg.exports_dir / "solution_summary.json", "w", encoding="utf-8") as f:
+            json.dump(
+                {k: (str(v) if not isinstance(v, (int, float, str, list, dict, bool, type(None))) else v)
+                 for k, v in summary.items()},
+                f, ensure_ascii=False, indent=2,
+            )
+
+        logger.info("Outputs saved to %s", cfg.schedules_dir)

src/routing_engine/quality.py

@@ -0,0 +1,105 @@
+"""Data quality checks on OFRAGs and fleet tables."""
+
+from __future__ import annotations
+
+import logging
+from typing import Dict, List
+
+import pandas as pd
+
+logger = logging.getLogger(__name__)
+
+
+def check_ofrags(ofrags: pd.DataFrame) -> Dict:
+    issues: List[str] = []
+
+    if ofrags.empty:
+        return {"ok": False, "issues": ["OFRAG table is empty"]}
+
+    missing_dep = ofrags["departure"].isna().sum()
+    if missing_dep:
+        issues.append(f"{missing_dep} OFRAGs have missing departure datetime")
+
+    missing_arr = ofrags["arrival"].isna().sum()
+    if missing_arr:
+        issues.append(f"{missing_arr} OFRAGs have missing arrival datetime")
+
+    neg_fh = (ofrags["flight_hours"] <= 0).sum()
+    if neg_fh:
+        issues.append(f"{neg_fh} OFRAGs have non-positive flight hours")
+
+    inverted = (ofrags["arrival"] <= ofrags["departure"]).sum()
+    if inverted:
+        issues.append(f"{inverted} OFRAGs have arrival ≤ departure")
+
+    not_base = (~ofrags["starts_at_base"] | ~ofrags["ends_at_base"]).sum()
+    if not_base:
+        issues.append(
+            f"{not_base} OFRAGs do not start AND end at the maintenance base "
+            f"(filtered out in load_all)"
+        )
+
+    ok = len(issues) == 0
+    if ok:
+        logger.info("OFRAG quality: OK (%d OFRAGs)", len(ofrags))
+    else:
+        for iss in issues:
+            logger.warning("OFRAG quality: %s", iss)
+
+    return {"ok": ok, "issues": issues}
+
+
+def check_fleet(fleet: pd.DataFrame) -> Dict:
+    issues: List[str] = []
+
+    if fleet.empty:
+        return {"ok": False, "issues": ["Fleet table is empty"]}
+
+    no_checks = fleet["checks"].apply(lambda c: len(c) == 0).sum()
+    if no_checks:
+        issues.append(f"{no_checks} aircraft have no maintenance check cycles")
+
+    zero_ttm = (fleet["ttm_hours"] <= 0).sum()
+    if zero_ttm:
+        issues.append(f"{zero_ttm} aircraft have TTM ≤ 0 (overdue maintenance)")
+
+    ok = len(issues) == 0
+    if ok:
+        logger.info("Fleet quality: OK (%d aircraft)", len(fleet))
+    else:
+        for iss in issues:
+            logger.warning("Fleet quality: %s", iss)
+
+    return {"ok": ok, "issues": issues}
+
+
+def check_feasibility(ofrags: pd.DataFrame, fleet: pd.DataFrame) -> Dict:
+    """High-level feasibility check before running the optimiser."""
+    issues: List[str] = []
+
+    if fleet.empty or ofrags.empty:
+        return {"ok": False, "issues": ["Empty inputs"]}
+
+    max_ttm = fleet["checks"].apply(
+        lambda cycles: max((c["ttm"] for c in cycles), default=0)
+    ).max()
+
+    infeasible_ofrags = ofrags[ofrags["flight_hours"] > max_ttm]
+    if not infeasible_ofrags.empty:
+        ids = infeasible_ofrags["ofrag_id"].tolist()
+        issues.append(
+            f"OFRAGs {ids} have flight_hours > max achievable TTM ({max_ttm:.1f} FH) "
+            f"– these cannot be served by any aircraft."
+        )
+
+    ok = len(issues) == 0
+    return {"ok": ok, "issues": issues}
+
+
+def run_all(ofrags: pd.DataFrame, fleet: pd.DataFrame) -> Dict:
+    r1 = check_ofrags(ofrags)
+    r2 = check_fleet(fleet)
+    r3 = check_feasibility(ofrags, fleet)
+    ok = r1["ok"] and r2["ok"] and r3["ok"]
+    issues = r1["issues"] + r2["issues"] + r3["issues"]
+    return {"ok": ok, "issues": issues}