Refactor: Implement Arara OARMP architecture and dynamic editors

.gitignore

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+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv
+
+# Outputs and Databases
+outputs/schedules/*.db
+outputs/schedules/*.csv
+outputs/schedules/*.txt
+outputs/exports/
+
+# Old local databases (just in case)
+demands.db
+optimized_cost.txt
+resultado_otimizacao_alocacoes*.csv

.python-version

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+3.12

APP.md

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+Você é um especialista em UI/UX usando Streamlit.
+
+[OBJETIVO]
+
+- Construir uma interface gráfica para o arquivo main.py.
+
+[REGRAS]
+
+- A interface deve ser clean, moderna e intuitiva.
+- Não crie gráficos que não sejam úteis.
+
+[WORKSPACE]
+
+- Você está em um ambiente Linux;
+- O shell é fish;
+- Coloque 'rtk' antes de todos os comandos;
+- O ambiente venv foi criado usando 'uv';
+- Rode scripts python com 'uv run ...';
+- Adicione bibliotecas com 'uv add ...';
+
+[PROJETO]
+
+- Faça a interface toda em inglês;
+- Crie uma aba chamada 'Data' com uma tabela do csv após filtragem e tratamento das variáveis;
+- Crie uma aba chamada 'Fleet' com a tabela df_frota;
+- Crie uma aba chamada 'Demand' com a tabela tabela_pax;
+- Crie uma aba chamada 'Solver' com sliders para os parâmetros do problema:
+  - Frota de aeronaves;
+  - Período de operação;
+  - Local de origem;
+  - Local de destino;
+  - Número de aeronaves de cada tipo;
+  - Número de dias;
+  - Número de voos;
+  - Número de passageiros;
+  - Número de tripulantes;
+  - Número de bagagens;
+  - Número de aeronaves de cada tipo;
+- Dentro da aba Solver coloque também, antes dos slides, o problema em 'matematiques' usando latex;
+- No final da aba Solver, coloque um botão para resolver e uma caixa de texto com a saída do solver;
+- Crie uma aba chamada 'Mapa' com um mapa da localização dos aeroportos na solução, com um slider por dia do ano;
+- Crie uma aba chamada 'Resultados' com os resultados do solver, em tabelas. Coloque também o resultado não otimizado;
+- Eu preciso saber também a quantiade de pernoites de uma aeronave em cada aeroporto.
+- Desenhe a rota, ao selecionar a rota, um pop-up deve aparecer com informações sobre a rota (incluindo a aeronave e quantidade de pax e dias fora de sede);
+- Mude a cor da rota caso seja uma rota de algum dia fora de sede.
+- Crie um novo arquivo para este projeto, chamado main_app.py
+- Utilize somente o Modelo VII
+- Deixe o código bem documento usando doxygen
+- Programe como um programado Senior usando Lean Code
+- Antes de prosseguir, me mostre uma comparação para este projeto: Streamlit ou Shiny e eu decidirei qual o melhor.

README.md

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+# Fleet Assignment Optimization - Força Aérea Brasileira
+
+## Integrantes do Grupo
+- 1T Lago
+- 1T Figueiredo
+- 1T Fialho
+
+## Sobre o Projeto
+O presente trabalho aborda o problema clássico de **Fleet Assignment** (Alocação de Frota) aplicado ao contexto do transporte logístico aéreo militar. 
+
+O sistema foi desenvolvido como uma aplicação interativa (Dashboard Web) acoplada a um modelo matemático avançado. O objetivo é designar de forma ótima os diferentes modelos de aeronaves de transporte (C-97, C-95M, C-105, KC-390, KC-30, C-99A, C-98 e C-98A) de seus respectivos Esquadrões para atender à matriz de passageiros solicitada. 
+
+A arquitetura equilibra a maximização do atendimento de passageiros com a **minimização dos custos logísticos** (consumo de combustível em litros e penalidades de pernoite em bases externas), tudo isso balizado por limites de alcance, velocidades, tamanho físico das frotas disponíveis nas bases (Emissores) e capacidade de passageiros de cada aeronave.
+
+### O Modelo Matemático
+O núcleo de otimização foi construído utilizando Programação Linear Inteira Mista (MILP) processada pelo solver **SCIP** (implementado com *Google OR-Tools*). A modelagem contempla:
+
+- **Função Objetivo:** $\min \sum_{t} \sum_{r} (c_{r} \cdot x_{r,t}) + \sum_{t} \sum_{d} (M \cdot s_{d,t})$
+  O algoritmo minimiza o custo total de combustível das missões ($c_r$), somado à grande penalidade ($M$) associada às variáveis de folga ($s_{d,t}$), forçando a malha a alocar missões para não deixar passageiros para trás, sempre que exequível.
+- **Restrições Principais:**
+  - **Atendimento de Demanda:** $\sum (\text{cap}_{m} \cdot x) + s \ge \text{PAX}$
+  - **Bloqueio Temporal de Frota:** Se uma aeronave realizar uma missão que exige pernoite fora de sua base, o modelo garante o "bloqueio" desse ativo nos dias subsequentes da janela temporal ($T$), impossibilitando a clonagem da aeronave.
+  - **Limites de Estoque:** As decolagens originadas de uma localidade jamais podem exceder a propriedade física do $\text{MaxFleet}$ distribuída para o esquadrão emissor.
+
+### Resultados Obtidos
+A ferramenta é capaz de rodar a otimização global e extrair métricas gerenciais comparativas:
+- **Baseline Fuel (Não-Otimizado):** O sistema isola qual seria o consumo nominal caso as demandas fossem atendidas de modo bruto.
+- **Optimized Fuel Consumption:** Retorna o custo de combustível estrito gerado pelo motor SCIP.
+- **Total Savings:** Revela à chefia o percentual quantitativo de economia gerada pela roteirização matemática contra o plano base.
+- **Visão Tática:** A malha de voos diária pode ser "escrutinada" visualmente por meio de um Mapa Interativo renderizado sobre CartoDB / OpenStreetMap, evidenciando as decisões de despacho por código ICAO.
+
+---
+
+## 📂 Arquitetura do Projeto (Arara OARMP)
+O projeto foi estruturado utilizando padrões modernos de software:
+```text
+/
+├── app/
+│   └── dashboard.py          # Interface Streamlit Interativa
+├── raw/                      # Dados brutos fixos e mapas geo-referenciados
+├── src/
+│   └── fleet_assignment/     # Cérebro Matemático
+│       ├── config.py         # Constantes Globais
+│       ├── ingest.py         # Leitura, tratamento e merge
+│       └── optimizer.py      # Motor MILP (SCIP / OR-Tools)
+├── outputs/                  # Banco de dados temporário e CSVs finais
+├── README.md
+├── pyproject.toml
+└── uv.lock
+```
+
+---
+
+## 🛠️ Guia de Instalação e Execução
+
+### 1. Instalar o Python (3.12+)
+Abra seu terminal/prompt e utilize o gerenciador de pacotes nativo do seu Sistema Operacional:
+
+* **Windows (Winget):** `winget install Python.Python.3.12`
+* **Ubuntu/Debian (APT):** `sudo apt update && sudo apt install python3 python3-pip python3-venv`
+* **Fedora (DNF):** `sudo dnf install python3`
+* **Arch Linux (Pacman):** `sudo pacman -S python`
+* **macOS (Homebrew):** `brew install python`
+
+### 2. Instalar o Gerenciador `uv`
+O projeto adota o `uv` (gerenciador de ambiente e dependências ultrarrápido em Rust):
+
+* **macOS / Linux:**
+  ```bash
+  curl -LsSf https://astral.sh/uv/install.sh | sh
+  ```
+* **Windows (PowerShell):**
+  ```powershell
+  powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
+  ```
+
+### 3. Configurar Ambiente e Executar
+Abra o terminal na pasta raiz deste projeto (onde este `README.md` e o arquivo `pyproject.toml` se encontram) e siga os passos abaixo:
+
+1. **Sincronizar Dependências (Instalar tudo e criar a VENV automaticamente):**
+   ```bash
+   rtk uv sync
+   ```
+2. **Ligar o Servidor Web e a Interface:**
+   ```bash
+   rtk uv run streamlit run app/dashboard.py
+   ```
+
+> **Acesso:** Assim que o servidor subir, o Python acionará automaticamente a abertura de uma nova guia no seu **navegador web padrão** acessando o painel de operações (normalmente via `http://localhost:8501`).

app/dashboard.py

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+"""
+Streamlit Dashboard for Brazilian Air Force Fleet Assignment.
+Provides interactive mapping, scenario generation, and SCIP solver integration.
+"""
+
+import os
+import json
+import sqlite3
+import subprocess
+import datetime
+import pandas as pd
+import pydeck as pdk
+import streamlit as st
+from vincenty import vincenty
+
+# ==========================================
+# PAGE CONFIGURATION
+# ==========================================
+st.set_page_config(
+    page_title="Fleet Assignment",
+    page_icon="✈️",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# ==========================================
+# CONSTANTS & CONFIGURATION
+# ==========================================
+import sys
+# Ensure we can import from src
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from src.fleet_assignment.config import (
+    CSV_FILEPATH, JSON_AEROPORTOS, JSON_EMISSORES, DB_PATH,
+    RESULTS_FILE, COST_FILE, AIRCRAFT_CONFIG
+)
+
+from src.fleet_assignment.ingest import load_data
+
+@st.cache_data
+def get_cached_data():
+    return load_data()
+
+demands_raw, df_fleet, valid_icaos, airports_geo = get_cached_data()
+
+# ==========================================
+# DATABASE MANAGEMENT
+# ==========================================
+def execute_sql(query, params=(), fetch=False):
+    """Helper to execute SQL commands cleanly."""
+    with sqlite3.connect(DB_PATH) as conn:
+        if fetch:
+            return pd.read_sql_query(query, conn, params=params)
+        conn.execute(query, params)
+        conn.commit()
+
+def init_db(demands_df, frota_df, force_reset=False):
+    """Initializes SQLite Database as the single source of truth."""
+    if force_reset and os.path.exists(DB_PATH):
+        os.remove(DB_PATH)
+    
+    with sqlite3.connect(DB_PATH) as conn:
+        conn.execute('''
+            CREATE TABLE IF NOT EXISTS demandas (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                data_apenas TEXT,
+                local_dec TEXT,
+                local_pou TEXT,
+                pax INTEGER
+            )
+        ''')
+        
+        count = conn.execute("SELECT COUNT(*) FROM demandas").fetchone()[0]
+        if count == 0:
+            df_insert = demands_df[['Data Apenas', 'Localidade Decolagem', 'Localidade Pouso', 'PAX']].copy()
+            df_insert.columns = ['data_apenas', 'local_dec', 'local_pou', 'pax']
+            df_insert.to_sql('demandas', conn, if_exists='append', index=False)
+            frota_df.to_sql('frota', conn, if_exists='replace', index=False)
+
+def load_demands():
+    return execute_sql("SELECT id as ID, data_apenas as 'Date', local_dec as 'Dep', local_pou as 'Arr', pax as 'PAX' FROM demandas", fetch=True)
+
+init_db(demands_raw, df_fleet)
+
+@st.cache_data
+def load_results():
+    try:
+        return pd.read_csv(RESULTS_FILE, sep=';')
+    except FileNotFoundError:
+        return pd.DataFrame()
+
+df_results = load_results()
+
+# ==========================================
+# SOLVER INTEGRATION
+# ==========================================
+def run_solver_subprocess(time_limit_sec, output_placeholder):
+    """Spawns the optimization worker and streams logs to the UI."""
+    process = subprocess.Popen(
+        ["uv", "run", "python", "-u", "solver_worker.py", str(time_limit_sec)],
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        text=True,
+        bufsize=1
+    )
+    
+    full_log = ""
+    for line in iter(process.stdout.readline, ''):
+        full_log += line
+        output_placeholder.code(full_log, language='text')
+        
+    process.wait()
+    return process.returncode == 0
+
+# ==========================================
+# UI RENDER
+# ==========================================
+st.title("✈️ Fleet Assignment")
+st.markdown("Brazilian Air Force Fleet Routing and Scheduling (Global Time-Space Network Flow)")
+
+tab1, tab2, tab3, tab4, tab5 = st.tabs(["Demand Data", "Fleet", "Solver", "Map", "Results"])
+
+# --- TAB 1: DEMAND DATA ---
+with tab1:
+    col_title, col_reset = st.columns([0.8, 0.2])
+    with col_title:
+        st.header("Passenger Demands")
+    with col_reset:
+        if st.button("🔄 Reset DB to Raw CSV", type="secondary", use_container_width=True):
+            init_db(demands_raw, df_fleet, force_reset=True)
+            st.success("Database Reset!")
+            st.rerun()
+            
+    df_display_demand = load_demands()
+    
+    st.info("💡 You can edit, add, or delete rows directly in the table below. Click 'Save Changes' when done.")
+    edited_demand = st.data_editor(
+        df_display_demand,
+        num_rows="dynamic",
+        use_container_width=True,
+        hide_index=True,
+        key="demand_editor"
+    )
+    
+    if st.button("💾 Save Demand Changes", type="primary", use_container_width=True):
+        with sqlite3.connect(DB_PATH) as conn:
+            conn.execute("DROP TABLE IF EXISTS demandas")
+            conn.execute('''
+                CREATE TABLE demandas (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    data_apenas TEXT,
+                    local_dec TEXT,
+                    local_pou TEXT,
+                    pax INTEGER
+                )
+            ''')
+            df_to_save = edited_demand.drop(columns=['ID'], errors='ignore')
+            df_to_save.columns = ['data_apenas', 'local_dec', 'local_pou', 'pax']
+            df_to_save.to_sql('demandas', conn, if_exists='append', index=False)
+        st.success("Database synchronized successfully!")
+        st.rerun()
+
+# --- TAB 2: FLEET ---
+with tab2:
+    st.header("Fleet Specifications")
+    
+    # Check if we have modified fleet in DB, otherwise use default
+    with sqlite3.connect(DB_PATH) as conn:
+        try:
+            current_fleet = pd.read_sql_query("SELECT * FROM frota", conn)
+        except Exception:
+            current_fleet = df_fleet.copy()
+            
+    df_display_fleet = current_fleet[['Modelo', 'Emissor', 'Tamanho da Frota', 'Consumo (km/l)', 'Velocidade Média (km/h)', 'Capacidade', 'Alcance']].copy()
+    df_display_fleet.columns = ['Modelo', 'Squadron', 'Fleet Size', 'Fuel Usage (km/l)', 'Mean Speed (km/h)', 'PAX Capacity', 'Max Range (km)']
+    
+    st.info("💡 Edit specs directly below to simulate larger fleets, different fuel burn, or cargo capacity.")
+    edited_fleet = st.data_editor(
+        df_display_fleet,
+        use_container_width=True,
+        hide_index=True,
+        key="fleet_editor"
+    )
+    
+    if st.button("💾 Save Fleet Changes", type="primary", use_container_width=True):
+        rename_back = {
+            'Modelo': 'Modelo', 'Squadron': 'Emissor', 'Fleet Size': 'Tamanho da Frota',
+            'Fuel Usage (km/l)': 'Consumo (km/l)', 'Mean Speed (km/h)': 'Velocidade Média (km/h)',
+            'PAX Capacity': 'Capacidade', 'Max Range (km)': 'Alcance'
+        }
+        df_save_fleet = edited_fleet.rename(columns=rename_back)
+        
+        with sqlite3.connect(DB_PATH) as conn:
+            old_frota = pd.read_sql_query("SELECT * FROM frota", conn)
+            for idx, row in df_save_fleet.iterrows():
+                mask = (old_frota['Modelo'] == row['Modelo']) & (old_frota['Emissor'] == row['Emissor'])
+                old_frota.loc[mask, 'Tamanho da Frota'] = row['Tamanho da Frota']
+                old_frota.loc[mask, 'Consumo (km/l)'] = row['Consumo (km/l)']
+                old_frota.loc[mask, 'Velocidade Média (km/h)'] = row['Velocidade Média (km/h)']
+                old_frota.loc[mask, 'Capacidade'] = row['Capacidade']
+                old_frota.loc[mask, 'Alcance'] = row['Alcance']
+            old_frota.to_sql('frota', conn, if_exists='replace', index=False)
+            
+        st.success("Fleet changes saved successfully!")
+        st.rerun()
+
+# --- TAB 3: SOLVER ---
+with tab3:
+    st.header("Mathematical Formulation")
+    st.latex(r"\min \sum_{t} \sum_{r} (c_{r} \cdot x_{r,t}) + \sum_{t} \sum_{d} (M \cdot s_{d,t})")
+    st.latex(r"\text{s.t.} \quad \sum_{m,e} (\text{cap}_{m} \cdot x_{m,e,r,t}) + s_{r,t} \ge \text{PAX}_{r,t} \quad \forall r, t")
+    st.latex(r"\sum_{t_{start} \le T \le t_{start} + \text{pernoites}} x_{m,e,r,t_{start}} \le \text{MaxFleet}_{m,e} \quad \forall m, e, T")
+    
+    st.markdown(r"""
+    **Dictionary of Variables:**
+    - $x_{m,e,r,t}$: Decision Variable (Integer). Number of aircraft of model $m$, from squadron $e$, allocated to route $r$ on day $t$.
+    - $c_r$: Estimated total fuel cost for the route $r$ mission, including overnight stay penalties if applicable.
+    - $s_{r,t}$: Slack Variable. Represents the passenger demand that could **not** be met on that day due to fleet limitations.
+    - $M$: High Penalty (Big M). Imposed on the system for each unit of slack activated, forcing the solver to meet demand whenever possible.
+    - $\text{cap}_m$: Maximum passenger capacity (seats) of aircraft $m$.
+    - $\text{PAX}_{r,t}$: Actual number of Air Force passengers needing to fly on route $r$ on day $t$.
+    - $\text{MaxFleet}_{m,e}$: Total physical aircraft available in squadron $e$ for model $m$.
+    - $T$: Temporal inspection window. Ensures aircraft blockade during overnight stays, preventing fleet duplication.
+    """)
+    
+    if st.button("🚀 Run Global Optimization", type="primary"):
+        st.info("Starting Worker... Follow the real-time SCIP log below:")
+        log_placeholder = st.empty()
+        
+        if run_solver_subprocess(300, log_placeholder):
+            st.success("Solver Finished! Results saved to CSV.")
+            st.session_state['new_results'] = True
+        else:
+            st.error("Solver Failed or was aborted.")
+
+# --- TAB 4: MAP ---
+with tab4:
+    st.header("Interactive Flight Map")
+    
+    if st.session_state.get('new_results'):
+        df_results = load_results()
+        
+    view_state = pdk.ViewState(latitude=-15.78, longitude=-47.92, zoom=3.5, pitch=45)
+    layers = []
+        
+    if df_results.empty:
+        st.info("No optimization results yet. Run the Global Optimization to see routes.")
+    else:
+        df_results['Data'] = pd.to_datetime(df_results['Data'])
+        dias_disponiveis = sorted(df_results['Data'].dt.date.unique())
+        
+        # Safe initialization of map date
+        if 'current_map_date' not in st.session_state or st.session_state.current_map_date not in dias_disponiveis:
+             st.session_state.current_map_date = dias_disponiveis[0]
+             
+        current_idx = dias_disponiveis.index(st.session_state.current_map_date)
+        
+        # Navigation Controls
+        col_prev, col_drop, col_next = st.columns([1, 8, 1])
+        with col_prev:
+            st.markdown("<br/>", unsafe_allow_html=True)
+            if st.button("⬅️", disabled=current_idx==0, use_container_width=True):
+                st.session_state.current_map_date = dias_disponiveis[current_idx - 1]
+                st.rerun()
+        with col_drop:
+            selected_date = st.date_input("Select via Calendar", value=st.session_state.current_map_date)
+            if selected_date != st.session_state.current_map_date:
+                st.session_state.current_map_date = selected_date
+                st.rerun()
+        with col_next:
+            st.markdown("<br/>", unsafe_allow_html=True)
+            if st.button("➡️", disabled=current_idx>=len(dias_disponiveis)-1, use_container_width=True):
+                st.session_state.current_map_date = dias_disponiveis[current_idx + 1]
+                st.rerun()
+                
+        slider_date = st.select_slider("Or scrub through scheduled flight days", options=dias_disponiveis, value=st.session_state.current_map_date)
+        if slider_date != st.session_state.current_map_date:
+            st.session_state.current_map_date = slider_date
+            st.rerun()
+                
+        df_day = df_results[df_results['Data'].dt.date == st.session_state.current_map_date]
+        df_demands_map = load_demands()
+        
+        # Map Data Parsing
+        map_data = []
+        for _, row in df_day.iterrows():
+            orig, dest = row['Local Decolagem'], row['Local Pouso']
+            
+            if orig in airports_geo and dest in airports_geo:
+                lon1, lat1 = float(airports_geo[orig]['lon']), float(airports_geo[orig]['lat'])
+                lon2, lat2 = float(airports_geo[dest]['lon']), float(airports_geo[dest]['lat'])
+                
+                pernoites = row.get('Pernoites', 0)
+                color = [255, 65, 54, 200] if pernoites > 0 else [0, 116, 217, 200]
+                
+                # Calculate PAX sum for this route on this day
+                pax_mask = (df_demands_map['Dep'] == orig) & (df_demands_map['Arr'] == dest) & (df_demands_map['Date'] == str(st.session_state.current_map_date))
+                pax_count = df_demands_map[pax_mask]['PAX'].sum()
+                
+                map_data.append({
+                    "start": [lon1, lat1], "end": [lon2, lat2], "color": color,
+                    "modelo": row['Modelo'], "orig": orig, "dest": dest,
+                    "pax": int(pax_count), "pernoites": pernoites
+                })
+        
+        df_map = pd.DataFrame(map_data)
+        if df_map.empty:
+            st.info("No flights scheduled for this day.")
+        else:
+            layers.append(pdk.Layer(
+                "ArcLayer", data=df_map,
+                get_source_position="start", get_target_position="end",
+                get_source_color="color", get_target_color="color",
+                get_width=3, pickable=True
+            ))
+            
+    st.pydeck_chart(pdk.Deck(
+        layers=layers,
+        initial_view_state=view_state,
+        tooltip={
+            "html": "<b>✈️ Aircraft:</b> {modelo}<br/><b>📍 Route:</b> {orig} ➔ {dest}<br/><b>👥 PAX:</b> {pax}<br/><b>🌙 Out of Base:</b> {pernoites} days",
+            "style": {"backgroundColor": "steelblue", "color": "white"}
+        } if layers else None,
+        map_style='road'
+    ))
+    
+    # Selected Day Results Table
+    if not df_results.empty:
+        df_day_display = df_day.copy()
+        
+        # Bind PAX back into the table
+        df_day_display['PAX'] = df_day_display.apply(lambda r: df_demands_map[
+            (df_demands_map['Dep'] == r['Local Decolagem']) & 
+            (df_demands_map['Arr'] == r['Local Pouso']) & 
+            (df_demands_map['Date'] == str(st.session_state.current_map_date))
+        ]['PAX'].sum(), axis=1)
+        
+        df_day_display.rename(columns={
+            'Data': 'Date', 'Modelo': 'Model', 'Emissor': 'Squadron',
+            'Local Decolagem': 'Departure', 'Local Pouso': 'Arrival',
+            'Qtd Aeronaves': 'Allocated Fleet', 'Pernoites': 'Days Out of Base'
+        }, inplace=True)
+        
+        st.subheader(f"Flight Schedule for {st.session_state.current_map_date}")
+        st.dataframe(df_day_display, use_container_width=True)
+
+# --- TAB 5: RESULTS ---
+with tab5:
+    st.header("Optimization Results")
+    
+    try:
+        raw_fuel_df = execute_sql("SELECT SUM(\"Combustível\") as total FROM frota", fetch=True)
+        raw_fuel = raw_fuel_df['total'].iloc[0] if not raw_fuel_df.empty else 0
+        if pd.isna(raw_fuel): raw_fuel = 0
+        
+        opt_fuel = None
+        if os.path.exists(COST_FILE):
+            with open(COST_FILE, "r") as f:
+                opt_fuel = float(f.read().strip())
+                
+        colA, colB, colC = st.columns(3)
+        with colA:
+            st.metric(label="Baseline Fuel (Non-Optimized)", value=f"{raw_fuel:,.2f} L")
+        with colB:
+            if opt_fuel is not None:
+                st.metric(label="Optimized Fuel Consumption", value=f"{opt_fuel:,.2f} L", delta=f"{opt_fuel - raw_fuel:,.2f} L", delta_color="inverse")
+        with colC:
+            if opt_fuel is not None and raw_fuel > 0:
+                savings = (1 - (opt_fuel / raw_fuel)) * 100
+                st.metric(label="Total Fuel Savings", value=f"{savings:.2f}%")
+    except Exception:
+        pass
+        
+    if not df_results.empty:
+        st.dataframe(df_results, use_container_width=True)
+        
+        if 'Pernoites' in df_results.columns:
+            st.subheader("Days Out of Base (Pernoites) Breakdown")
+            df_pernoites = df_results[df_results['Pernoites'] > 0]
+            st.dataframe(df_pernoites[['Modelo', 'Local Decolagem', 'Local Pouso', 'Pernoites']].sort_values(by='Pernoites', ascending=False))
+    else:
+        st.warning("No results available.")

create_nb.py

@@ -1,286 +0,0 @@
-import nbformat as nbf
-
-nb = nbf.v4.new_notebook()
-
-cells = []
-
-# Cell 1: Markdown Introduction
-cells.append(nbf.v4.new_markdown_cell("""# Alocação de Frotas (Fleet Scheduling) - Aeronaves C-97
-
-Este projeto visa realizar a alocação de frota (Fleet Assignment) baseada nos princípios do livro referenciado *Airline Operations and Scheduling*.
-Diferentemente do modelo anterior que utilizava aeronaves de carga, este foca exclusivamente na aeronave de passageiros **C-97**.
-
-## Objetivos:
-1. Remover cálculos de aeronaves de carga.
-2. Analisar as demandas diárias para a aeronave C-97.
-3. Identificar os esquadrões detentores dessas aeronaves e a quantidade de aeronaves (matrículas únicas).
-4. Calcular os 5 trechos mais relevantes do ano de 2025 utilizando estatística e gráficos.
-5. Realizar o Fleet Assignment para os 5 trechos, visando minimizar o custo total da operação (Modelo 1 proposto).
-"""))
-
-# Cell 2: Imports
-cells.append(nbf.v4.new_code_cell("""# Importação de bibliotecas
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-import pulp
-import pymap3d as pm
-import math
-import networkx as nx
-
-# Configuração de estilo para os gráficos
-sns.set_theme(style="whitegrid")
-
-# Constantes globais
-CSV_FILEPATH = "SISCO_AEREA_01-01-2025_A_31-12-2025_1911251714Z.csv"
-JSON_AEROPORTOS = "airports.json"
-CAPACIDADE_C97 = 30 # Capacidade média de passageiros do C-97 (Brasília)
-"""))
-
-# Cell 3: Markdown Load Data
-cells.append(nbf.v4.new_markdown_cell("""## 1. Leitura e Limpeza dos Dados
-Vamos carregar os dados, filtrar apenas para o modelo `C-97` e tratar os campos de localidades e datas.
-"""))
-
-# Cell 4: Code Load Data
-cells.append(nbf.v4.new_code_cell("""# Carregamento do banco de dados
-df = pd.read_csv(CSV_FILEPATH, low_memory=False)
-df_aeroportos = pd.read_json(JSON_AEROPORTOS, orient='index')
-
-# Tratamento básico de valores nulos expressos como 'NIL'
-df.replace('NIL', 0, inplace=True)
-
-# Filtro para a aeronave C-97 (Passageiros) e remoção de registros de carga
-df_c97 = df[df['Modelo'] == 'C-97'].copy()
-
-# Filtrando decolagens e pousos conhecidos
-df_c97 = df_c97[df_c97['Localidade Decolagem'] != 0]
-df_c97 = df_c97[df_c97['Localidade Pouso'] != 0]
-
-# Criação da coluna Trecho
-df_c97['Trecho'] = df_c97['Localidade Decolagem'] + '-' + df_c97['Localidade Pouso']
-
-# Conversão da coluna PAX para numérico
-df_c97['PAX'] = pd.to_numeric(df_c97['PAX']).fillna(0)
-
-# Tratamento de datas
-df_c97['Dep TimeStamp'] = pd.to_datetime(df_c97['Data de Decolagem'] + ' ' + df_c97['Hora de Decolagem'], format='mixed', dayfirst=True)
-df_c97['Data Apenas'] = df_c97['Dep TimeStamp'].dt.date
-"""))
-
-# Cell 5: Markdown Squadrons
-cells.append(nbf.v4.new_markdown_cell("""## 2. Análise dos Esquadrões e Matrículas (Aeronaves)
-Identificaremos quais esquadrões operam o C-97 e qual a disponibilidade de aeronaves (número de matrículas únicas), pois isso definirá as restrições de frota.
-"""))
-
-# Cell 6: Code Squadrons
-cells.append(nbf.v4.new_code_cell("""# Obtendo esquadrões e matrículas únicas
-esquadroes = df_c97['Emissor'].unique()
-matriculas_unicas = df_c97['Matrícula'].unique()
-total_aeronaves = len(matriculas_unicas)
-
-print(f"Esquadrões que operam o C-97: {', '.join(esquadroes)}")
-print(f"Total de matrículas únicas (aeronaves disponíveis): {total_aeronaves}")
-
-# Aeronaves por esquadrão
-aeronaves_por_esquadrao = df_c97.groupby('Emissor')['Matrícula'].nunique().to_dict()
-print("\\nAeronaves por esquadrão:")
-for esq, qtd in aeronaves_por_esquadrao.items():
-    print(f" - {esq}: {qtd} aeronaves")
-    
-# Identificar a base principal de cada esquadrão (aeroporto com mais decolagens)
-bases_esquadroes = {}
-for esq in aeronaves_por_esquadrao.keys():
-    base = df_c97[df_c97['Emissor'] == esq]['Localidade Decolagem'].mode()[0]
-    bases_esquadroes[esq] = base
-
-print("\\nBases inferidas por esquadrão (pela moda de decolagens):")
-for esq, base in bases_esquadroes.items():
-    print(f" - {esq}: {base}")
-"""))
-
-# Cell 7: Markdown Stats
-cells.append(nbf.v4.new_markdown_cell("""## 3. Análise Estatística das Demandas Diárias
-Para definir os trechos mais relevantes do ano, calcularemos a demanda de passageiros (PAX) agregada diariamente. Em seguida, extraímos a **média de passageiros diários** e o **desvio padrão** para cada trecho. A relevância será determinada pela maior média de demanda diária de PAX.
-Começaremos focando em 5 trechos (alvo variável).
-"""))
-
-# Cell 8: Code Stats
-cells.append(nbf.v4.new_code_cell("""# Demanda diária por trecho
-demanda_diaria = df_c97.groupby(['Trecho', 'Data Apenas'])['PAX'].sum().reset_index()
-
-# Criar um range de datas para todo o ano de 2025 para contabilizar os dias sem voo
-import itertools
-datas_2025 = pd.date_range(start='2025-01-01', end='2025-12-31').date
-todos_trechos = demanda_diaria['Trecho'].unique()
-idx = pd.MultiIndex.from_product([todos_trechos, datas_2025], names=['Trecho', 'Data Apenas'])
-demanda_diaria_completa = pd.DataFrame(index=idx).reset_index()
-
-# Mesclar com os dados reais e preencher NaN com 0
-demanda_diaria_completa = pd.merge(demanda_diaria_completa, demanda_diaria, on=['Trecho', 'Data Apenas'], how='left').fillna({'PAX': 0})
-
-# Estatísticas (Média e Desvio Padrão) por trecho
-estatisticas_trechos = demanda_diaria_completa.groupby('Trecho').agg(
-    Media_PAX_Diario=('PAX', 'mean'),
-    Desvio_Padrao_PAX=('PAX', 'std'),
-    Total_PAX=('PAX', 'sum'),
-    Dias_Operados=('PAX', lambda x: (x > 0).sum())
-).reset_index()
-
-# Média PAX por dias operados (usado para o Fleet Assignment Model)
-estatisticas_trechos['Media_PAX_Dias_Operados'] = estatisticas_trechos.apply(
-    lambda row: row['Total_PAX'] / row['Dias_Operados'] if row['Dias_Operados'] > 0 else 0, axis=1
-)
-
-# Nova métrica solicitada: PAX * Dias_Operados
-estatisticas_trechos['Score_Relevancia'] = estatisticas_trechos['Total_PAX'] * estatisticas_trechos['Dias_Operados']
-
-# Ordenar pelas rotas com maior métrica (PAX * Dias_Operados)
-estatisticas_trechos = estatisticas_trechos.sort_values(by='Score_Relevancia', ascending=False)
-
-# Definir número de trechos alvo
-NUM_TRECHOS = 10
-top_trechos = estatisticas_trechos.head(NUM_TRECHOS)
-
-display(top_trechos)
-
-# Gráfico
-plt.figure(figsize=(10, 6))
-sns.barplot(data=top_trechos, x='Trecho', y='Score_Relevancia', hue='Trecho', palette='viridis', legend=False)
-plt.title(f'Top {NUM_TRECHOS} Trechos Mais Relevantes (PAX * Dias Operados)')
-plt.ylabel('Score de Relevância (PAX * Dias Operados)')
-plt.xlabel('Trecho (Origem - Destino)')
-plt.xticks(rotation=45)
-plt.tight_layout()
-plt.show()
-"""))
-
-# Cell: Markdown Diagrama de Rede
-cells.append(nbf.v4.new_markdown_cell("""## Diagrama de Rede dos Trechos
-Para melhor visualização dos trechos mais relevantes, abaixo temos um diagrama de rede conectando as origens aos destinos.
-"""))
-
-# Cell: Code Diagrama de Rede
-cells.append(nbf.v4.new_code_cell("""# Criação do grafo direcionado
-G = nx.DiGraph()
-
-# Adiciona arestas com peso baseado no Score de Relevância
-for _, row in top_trechos.iterrows():
-    origem, destino = row['Trecho'].split('-')
-    peso = row['Score_Relevancia']
-    G.add_edge(origem, destino, weight=peso)
-
-# Configuração do layout do grafo - k maior afasta mais os nós
-pos = nx.spring_layout(G, k=2.5, iterations=100, seed=42)
-
-plt.figure(figsize=(12, 8))
-# Define o tamanho do nó
-tamanho_no = 1200
-
-# Desenha os nós
-nx.draw_networkx_nodes(G, pos, node_size=tamanho_no, node_color='skyblue', edgecolors='black')
-# Desenha as arestas, informando o node_size para que a seta não fique escondida sob o nó
-nx.draw_networkx_edges(
-    G, pos, 
-    edgelist=G.edges(), 
-    arrows=True, 
-    arrowstyle='-|>', 
-    arrowsize=25, 
-    edge_color='gray', 
-    width=2, 
-    node_size=tamanho_no, 
-    connectionstyle='arc3,rad=0.15'
-)
-# Desenha os rótulos (nomes dos aeroportos)
-nx.draw_networkx_labels(G, pos, font_size=12, font_family='sans-serif', font_weight='bold')
-
-plt.title('Diagrama de Rede - Top Trechos Relevantes', fontsize=14)
-plt.axis('off')
-plt.tight_layout()
-plt.show()
-"""))
-
-# Cell 9: Markdown FAM
-cells.append(nbf.v4.new_markdown_cell("""## 4. Fleet Assignment Model (FAM)
-Conforme o livro base *Airline Operations and Scheduling*, o problema de alocação de frotas busca atribuir os tipos de aeronaves às pernas de voo visando minimizar o custo e as perdas de receita (spill). Como temos apenas um tipo (C-97), nosso problema adapta-se a uma **alocação de esquadrões (e suas respectivas bases)** aos voos requeridos para minimizar o custo total de operação, que é proporcional à distância voada.
-
-**Modelo Matemático:**
-- **Variáveis de decisão:** $X_{s, r}$ (Quantidade de voos operados pelo esquadrão $s$ no trecho $r$)
-- **Função Objetivo:** Minimizar a distância total (Distância de posicionamento da base + Distância do trecho + Retorno para a base).
-- **Restrições:**
-  1. O número de voos alocados para o trecho deve suprir a demanda média (Média PAX / Capacidade do C-97).
-  2. O total de voos atribuídos a um esquadrão não pode exceder o número de aeronaves disponíveis naquele esquadrão no dia.
-"""))
-
-# Cell 10: Code FAM
-cells.append(nbf.v4.new_code_cell("""# Preparação dos dados para o modelo
-rotas_lista = top_trechos['Trecho'].tolist()
-
-# Dicionário de distâncias
-def calc_distancia(icao1, icao2):
-    try:
-        lat1, lon1, alt1 = df_aeroportos.loc[icao1, ['lat', 'lon', 'elevation']]
-        lat2, lon2, alt2 = df_aeroportos.loc[icao2, ['lat', 'lon', 'elevation']]
-        # Retorna a distância oblíqua em km
-        return pm.geodetic2aer(lat1, lon1, alt1, lat2, lon2, alt2)[2] / 1000.0
-    except KeyError:
-        return 9999.0 # Penalidade caso aeroporto não exista no json
-
-distancias_voo = {}
-for r in rotas_lista:
-    origem, destino = r.split('-')
-    dist_trecho = calc_distancia(origem, destino)
-    for esq, base in bases_esquadroes.items():
-        dist_ida = calc_distancia(base, origem) if base != origem else 0
-        dist_volta = calc_distancia(destino, base) if base != destino else 0
-        distancias_voo[(esq, r)] = dist_ida + dist_trecho + dist_volta
-
-# Demanda de voos por rota (Média diária)
-voos_requeridos = {}
-for _, row in top_trechos.iterrows():
-    # Arredondamento para cima da (demanda / capacidade)
-    voos = math.ceil(row['Media_PAX_Dias_Operados'] / CAPACIDADE_C97)
-    # Pelo menos 1 voo para suprir a demanda da rota selecionada
-    voos_requeridos[row['Trecho']] = max(1, voos)
-
-# MODELAGEM COM PULP
-prob = pulp.LpProblem("Fleet_Assignment_C97", pulp.LpMinimize)
-
-# Variáveis
-X = pulp.LpVariable.dicts("X", [(esq, r) for esq in aeronaves_por_esquadrao.keys() for r in rotas_lista], lowBound=0, cat='Integer')
-
-# Função Objetivo
-prob += pulp.lpSum([distancias_voo[(esq, r)] * X[(esq, r)] for esq in aeronaves_por_esquadrao.keys() for r in rotas_lista])
-
-# Restrição 1: Suprir a demanda da rota
-for r in rotas_lista:
-    prob += pulp.lpSum([X[(esq, r)] for esq in aeronaves_por_esquadrao.keys()]) >= voos_requeridos[r]
-
-# Restrição 2: Limite de aeronaves por esquadrão
-for esq in aeronaves_por_esquadrao.keys():
-    prob += pulp.lpSum([X[(esq, r)] for r in rotas_lista]) <= aeronaves_por_esquadrao[esq]
-
-# Solução
-prob.solve(pulp.PULP_CBC_CMD(msg=False))
-
-# Exibição dos resultados
-print("== RESULTADO DO FLEET ASSIGNMENT ==")
-print(f"Status: {pulp.LpStatus[prob.status]}")
-print(f"Distância Total Minimizada: {pulp.value(prob.objective):.2f} km\\n")
-
-print("Alocações (Voos Diários):")
-for r in rotas_lista:
-    print(f"\\nTrecho: {r} (Voos necessários: {voos_requeridos[r]})")
-    for esq in aeronaves_por_esquadrao.keys():
-        qtd = int(X[(esq, r)].varValue)
-        if qtd > 0:
-            print(f" -> {esq} (Base {bases_esquadroes[esq]}): {qtd} voo(s)")
-"""))
-
-nb.cells = cells
-
-with open('modelos.ipynb', 'w', encoding='utf-8') as f:
-    nbf.write(nb, f)
-
-print("modelos.ipynb gerado com sucesso!")

pyproject.toml

@@ -13,5 +13,7 @@ dependencies = [
     "pulp>=3.3.2",
     "pymap3d>=3.2.0",
     "seaborn>=0.13.2",
+    "streamlit>=1.58.0",
+    "tabulate>=0.10.0",
     "vincenty>=0.1.4",
 ]

raw/emissores.json

@@ -0,0 +1,46 @@
+[
+    {
+        "1GAV8": "SBNT",
+        "2/2 GT": "SBGL",
+        "BABV": "SBBV",
+        "CINDACTA I": "SBBS",
+        "ETA7": "SBMN",
+        "1 GTT": "BAAN",
+        "3GAV8": "SBSC",
+        "1GAV3": "SBBV",
+        "1GAV12": "SBSM",
+        "PAMA LS": "SBLS",
+        "ETA3": "SBGL",
+        "ETA1": "SBBE",
+        "2GAV3": "SBPV",
+        "1 GAVCA": "SBSC",
+        "1GAV9": "SBMN",
+        "ETA5": "SBCO",
+        "1/1 GT": "SBGL",
+        "2GAV6": "SBAN",
+        "CINDACTA IV": "SBMN",
+        "1 GDA": "SBAN",
+        "EPCAR": "SBBQ",
+        "1GAV14": "SBCO",
+        "1GAV15": "SBCG",
+        "BASM": "SBSM",
+        "BACG": "SBCG",
+        "EEAR": "SBGW",
+        "3GAV7": "SBBE",
+        "PAMA SP": "SBMT",
+        "ETA2": "SBNT",
+        "1/2 GT": "SBGL",
+        "3GAV3": "SBCG",
+        "5GAV8": "SBSM",
+        "ETA6": "SBBR",
+        "1GAV7": "SBSC",
+        "CINDACTA II": "SBBI",
+        "BAPV": "SBPV",
+        "7GAV8": "SBMN",
+        "1GAV10": "SBSM",
+        "2GAV5": "SBNT",
+        "2GAV10": "SBCG",
+        "2GAV7": "SBCO",
+        "CINDACTA III": "SBRF"
+    }
+]

src/fleet_assignment/config.py

@@ -0,0 +1,26 @@
+import os
+
+# Project Roots
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+RAW_DIR = os.path.join(ROOT_DIR, 'raw')
+OUTPUTS_DIR = os.path.join(ROOT_DIR, 'outputs')
+SCHEDULES_DIR = os.path.join(OUTPUTS_DIR, 'schedules')
+
+# File Paths
+CSV_FILEPATH = os.path.join(RAW_DIR, "SISCO_AEREA_01-01-2025_A_31-12-2025_1911251714Z.csv")
+JSON_AEROPORTOS = os.path.join(RAW_DIR, "airports.json")
+JSON_EMISSORES = os.path.join(RAW_DIR, "emissores.json")
+DB_PATH = os.path.join(SCHEDULES_DIR, "demands.db")
+RESULTS_FILE = os.path.join(SCHEDULES_DIR, "resultado_otimizacao_alocacoes.csv")
+COST_FILE = os.path.join(SCHEDULES_DIR, "optimized_cost.txt")
+
+AIRCRAFT_CONFIG = {
+    'C-97':  {'cap': 30,  'range': 1600,  'daily_fuel': 240},
+    'C-95M': {'cap': 12,  'range': 1900,  'daily_fuel': 180},
+    'C-105': {'cap': 73,  'range': 5000,  'daily_fuel': 180},
+    'KC-390':{'cap': 80,  'range': 6000,  'daily_fuel': 180},
+    'KC-30': {'cap': 238, 'range': 14500, 'daily_fuel': 600},
+    'C-99A': {'cap': 50,  'range': 2200,  'daily_fuel': 180},
+    'C-98':  {'cap': 10,  'range': 2400,  'daily_fuel': 180},
+    'C-98A': {'cap': 14,  'range': 2400,  'daily_fuel': 180}
+}

src/fleet_assignment/ingest.py

@@ -0,0 +1,77 @@
+import json
+import pandas as pd
+from vincenty import vincenty
+import sys
+import os
+
+# Ensure src module is visible if run standalone
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+from src.fleet_assignment.config import (
+    CSV_FILEPATH, JSON_AEROPORTOS, JSON_EMISSORES, AIRCRAFT_CONFIG
+)
+
+def load_data():
+    """Reads raw CSV and JSON to build the baseline demand and fleet dataframes."""
+    df = pd.read_csv(CSV_FILEPATH, low_memory=False)
+    df_airports = pd.read_json(JSON_AEROPORTOS, orient='index')
+    
+    # Basic Cleaning
+    df.replace('NIL', 0, inplace=True)
+    df = df[(df['Localidade Decolagem'] != 0) & (df['Localidade Pouso'] != 0)]
+    df = df[df['Modelo'].isin(AIRCRAFT_CONFIG.keys())]
+    
+    df['PAX'] = pd.to_numeric(df['PAX']).fillna(0)
+    df['Dep TimeStamp'] = pd.to_datetime(df['Data de Decolagem'].astype(str) + ' ' + df['Hora de Decolagem'].astype(str), format='mixed', dayfirst=True, errors='coerce')
+    df['Data Apenas'] = df['Dep TimeStamp'].dt.date
+    df['Combustível'] = pd.to_numeric(df['Combustível'], errors='coerce').fillna(0)
+    
+    # Merge Coordinates
+    df = df.merge(df_airports[['lat', 'lon']], left_on='Localidade Decolagem', right_index=True, how='left')
+    df.rename(columns={'lat': 'lat_dep', 'lon': 'lon_dep'}, inplace=True)
+    df = df.merge(df_airports[['lat', 'lon']], left_on='Localidade Pouso', right_index=True, how='left')
+    df.rename(columns={'lat': 'lat_arr', 'lon': 'lon_arr'}, inplace=True)
+    
+    # Distance & Time Computations
+    def calc_dist(row):
+        if pd.isna(row['lat_dep']) or pd.isna(row['lat_arr']): return 0.0
+        return vincenty((row['lat_dep'], row['lon_dep']), (row['lat_arr'], row['lon_arr']))
+        
+    df['Distancia'] = df.apply(calc_dist, axis=1)
+    df['Tempo de Voo'] = pd.to_timedelta(df['Tempo de Voo'].astype(str), errors='coerce')
+    df['Tempo de Voo (h)'] = df['Tempo de Voo'].dt.total_seconds() / 3600.0
+
+    # Build Fleet Specifications
+    df_fleet = df.groupby(['Modelo', 'Emissor']).agg({
+        'Combustível': 'sum',
+        'Distancia': 'sum',
+        'Tempo de Voo (h)': 'sum',
+        'Matrícula': 'nunique'
+    })
+    df_fleet.rename(columns={'Matrícula': 'Tamanho da Frota'}, inplace=True)
+    
+    df_fleet['Consumo (km/l)'] = (df_fleet['Distancia'] / df_fleet['Combustível']).replace([float('inf'), -float('inf')], 0).fillna(0)
+    df_fleet['Velocidade Média (km/h)'] = (df_fleet['Distancia'] / df_fleet['Tempo de Voo (h)']).replace([float('inf'), -float('inf')], 0).fillna(0)
+    
+    models = df_fleet.index.get_level_values('Modelo')
+    df_fleet['Capacidade'] = models.map(lambda m: AIRCRAFT_CONFIG[m]['cap'])
+    df_fleet['Alcance'] = models.map(lambda m: AIRCRAFT_CONFIG[m]['range'])
+    df_fleet['Diaria_Litros'] = models.map(lambda m: AIRCRAFT_CONFIG[m]['daily_fuel'])
+
+    # Build Demands
+    df_valid = df.dropna(subset=['Data Apenas']).copy()
+    df_valid = df_valid[(df_valid['Localidade Decolagem'] != '0') & (df_valid['Localidade Pouso'] != '0')]
+    demands_grouped = df_valid.groupby(['Data Apenas', 'Localidade Decolagem', 'Localidade Pouso'])['PAX'].sum().reset_index()
+    demands = demands_grouped[demands_grouped['PAX'] > 0].copy()
+
+    # Load External Lookups
+    with open(JSON_EMISSORES, 'r') as f:
+        emissores_data = json.load(f)[0]
+    
+    with open(JSON_AEROPORTOS, 'r') as f:
+        airports_geo = json.load(f)
+        
+    # Validation Lists
+    valid_icaos = df_airports.index.intersection(list(set(demands['Localidade Decolagem'].unique().tolist() + demands['Localidade Pouso'].unique().tolist() + list(emissores_data.values())))).tolist()
+    
+    return demands, df_fleet.reset_index(), valid_icaos, airports_geo

src/fleet_assignment/optimizer.py

@@ -0,0 +1,192 @@
+"""
+Worker script to run the SCIP solver in a separate process.
+This allows capturing the C++ stdout (EnableOutput) and streaming it back to Streamlit.
+"""
+
+import sys
+import pandas as pd
+import math
+import datetime
+from ortools.linear_solver import pywraplp
+import json
+from vincenty import vincenty
+
+import os
+import sqlite3
+
+# Ensure src module is visible if run standalone
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+from src.fleet_assignment.config import DB_PATH, RESULTS_FILE, COST_FILE
+
+def run_solver(time_limit_sec=300):
+    print(f"--- Starting Global Solver Worker (Limit: {time_limit_sec}s) ---")
+    JSON_AEROPORTOS = "airports.json"
+    
+    conn = sqlite3.connect(DB_PATH)
+    df_frota_db = pd.read_sql_query("SELECT * FROM frota", conn)
+    demands_db = pd.read_sql_query("SELECT data_apenas as 'Data Apenas', local_dec as 'Localidade Decolagem', local_pou as 'Localidade Pouso', pax as 'PAX' FROM demandas", conn)
+    conn.close()
+    
+    df_aeroportos = pd.read_json(JSON_AEROPORTOS, orient='index')
+    
+    # Format demands as required by downstream logic
+    pax_demanda_completa = demands_db.groupby(['Data Apenas', 'Localidade Decolagem', 'Localidade Pouso'])['PAX'].sum().reset_index()
+    demands = pax_demanda_completa[pax_demanda_completa['PAX'] > 0].copy()
+
+    with open('emissores.json', 'r') as f:
+        emissores_data = json.load(f)[0]
+    icaos_emissores = list(emissores_data.values())
+    
+    icaos_dec = demands['Localidade Decolagem'].unique().tolist()
+    icaos_pou = demands['Localidade Pouso'].unique().tolist()
+    all_icaos = [icao for icao in list(set(icaos_dec + icaos_pou + icaos_emissores)) if str(icao).strip() not in ['0', '']]
+    
+    coords = df_aeroportos.loc[df_aeroportos.index.intersection(all_icaos), ['lat', 'lon']]
+    valid_icaos = coords.index.tolist()
+    
+    dist_matrix = pd.DataFrame(index=valid_icaos, columns=valid_icaos)
+    for i in valid_icaos:
+        for j in valid_icaos:
+            if i == j: dist_matrix.loc[i, j] = 0.0
+            else:
+                lat1, lon1 = coords.loc[i, 'lat'], coords.loc[i, 'lon']
+                lat2, lon2 = coords.loc[j, 'lat'], coords.loc[j, 'lon']
+                dist_matrix.loc[i, j] = round(vincenty((lat1, lon1), (lat2, lon2)), 2)
+
+    AERONAVES_INTERESSE = ['C-97', 'C-95M', 'C-105', 'KC-390', 'KC-30', 'C-99A', 'C-98', 'C-98A']
+    CAPACIDADES_AERONAVES_INTERESSE = [30, 12, 73, 80, 238, 50, 10, 14]
+    ALCANCE_AERONAVES_INTERESSE = [1600, 1900, 5000, 6000, 14500, 2200, 2400, 2400]
+    DIARIAS_AERONAVES_INTERESSE = [x * 60 for x in [4, 3, 3, 3, 10, 3, 3, 3]]
+
+    frotas = []
+    for idx, row in df_frota_db.iterrows():
+        modelo, emissor = row['Modelo'], row['Emissor']
+        consumo = row['Consumo (km/l)']
+        if consumo > 0 and modelo in AERONAVES_INTERESSE:
+            a_idx = AERONAVES_INTERESSE.index(modelo)
+            frotas.append({
+                'modelo': str(modelo),
+                'emissor': str(emissor),
+                'base': emissores_data.get(str(emissor), None),
+                'frota_max': int(row['Tamanho da Frota']),
+                'consumo_kml': float(consumo),
+                'capacidade': int(CAPACIDADES_AERONAVES_INTERESSE[a_idx]),
+                'alcance': float(ALCANCE_AERONAVES_INTERESSE[a_idx]),
+                'velocidade_media': float(row['Velocidade Média (km/h)']),
+                'diaria_litros': float(DIARIAS_AERONAVES_INTERESSE[a_idx])
+            })
+    frotas = [f for f in frotas if f['base'] is not None and f['base'] in valid_icaos]
+
+    df_dias = pd.to_datetime(demands['Data Apenas']).dt.date
+    dias_unicos_list = sorted(df_dias.dropna().unique())
+    if not dias_unicos_list:
+        print("NO DEMAND DATA.")
+        return
+        
+    min_date = min(dias_unicos_list)
+    max_date = max(dias_unicos_list)
+    todas_as_datas = [min_date + datetime.timedelta(days=i) for i in range((max_date - min_date).days + 1)]
+    
+    print("Pre-computing distances and overnight stays...")
+    unique_routes = demands[['Localidade Decolagem', 'Localidade Pouso']].drop_duplicates()
+    route_info = {}
+    for idx, row in unique_routes.iterrows():
+        l_dec = str(row['Localidade Decolagem'])
+        l_pou = str(row['Localidade Pouso'])
+        if l_dec not in valid_icaos or l_pou not in valid_icaos:
+            continue
+        for f in frotas:
+            m, e, base = f['modelo'], f['emissor'], f['base']
+            dist_total = dist_matrix.loc[base, l_dec] + dist_matrix.loc[l_dec, l_pou] + dist_matrix.loc[l_pou, base]
+            tempo_missao = dist_total / f['velocidade_media'] if f['velocidade_media'] > 0 else float('inf')
+            
+            if tempo_missao <= 96.0:
+                num_pernoites = max(0, math.ceil(tempo_missao / 12.0) - 1)
+                fator1 = 1.25 if dist_matrix.loc[base, l_dec] > f['alcance'] else 1.0
+                fator2 = 1.25 if dist_matrix.loc[l_dec, l_pou] > f['alcance'] else 1.0
+                fator3 = 1.25 if dist_matrix.loc[l_pou, base] > f['alcance'] else 1.0
+                comb_missao = ((dist_matrix.loc[base, l_dec]*fator1 + dist_matrix.loc[l_dec, l_pou]*fator2 + dist_matrix.loc[l_pou, base]*fator3) / f['consumo_kml']) + (num_pernoites * f['diaria_litros'])
+                route_info[(m, e, l_dec, l_pou)] = {'num_pernoites': num_pernoites, 'combustivel_missao': comb_missao}
+
+    solver = pywraplp.Solver.CreateSolver('SCIP')
+    solver.SetTimeLimit(time_limit_sec * 1000)
+    solver.EnableOutput() # O MAIS IMPORTANTE: Isso escreve no stdout para o subprocess ler!
+    
+    print("Building Demand Matrix...")
+    x, s = {}, {}
+    for t in dias_unicos_list:
+        demandas_dia = demands[df_dias == t]
+        for idx, d_row in demandas_dia.iterrows():
+            l_dec, l_pou = str(d_row['Localidade Decolagem']), str(d_row['Localidade Pouso'])
+            if l_dec not in valid_icaos or l_pou not in valid_icaos: continue
+            
+            s[(l_dec, l_pou, t)] = solver.NumVar(0, solver.infinity(), f"s_{l_dec}_{l_pou}_{t}")
+            restricao_pax = solver.Constraint(float(d_row['PAX']), solver.infinity(), "")
+            restricao_pax.SetCoefficient(s[(l_dec, l_pou, t)], 1)
+            
+            for f in frotas:
+                m, e = f['modelo'], f['emissor']
+                if (m, e, l_dec, l_pou) in route_info:
+                    x[(m, e, l_dec, l_pou, t)] = solver.IntVar(0, solver.infinity(), f"x_{m}_{e}_{l_dec}_{l_pou}_{t}")
+                    restricao_pax.SetCoefficient(x[(m, e, l_dec, l_pou, t)], f['capacidade'])
+
+    print("Building Fleet Temporal Matrix...")
+    for f in frotas:
+        m, e, frota_max = f['modelo'], f['emissor'], f['frota_max']
+        for t_atual in todas_as_datas:
+            restricao_frota = solver.Constraint(0, frota_max, "")
+            for t_start in dias_unicos_list:
+                diff_days = (t_atual - t_start).days
+                if 0 <= diff_days <= 7:
+                    for idx, d_row in demands[df_dias == t_start].iterrows():
+                        l_dec, l_pou = str(d_row['Localidade Decolagem']), str(d_row['Localidade Pouso'])
+                        if (m, e, l_dec, l_pou, t_start) in x:
+                            if diff_days <= route_info[(m, e, l_dec, l_pou)]['num_pernoites']:
+                                restricao_frota.SetCoefficient(x[(m, e, l_dec, l_pou, t_start)], 1)
+
+    print("\n--- STARTING SCIP OPTIMIZATION ---\n")
+    sys.stdout.flush()
+    
+    objetivo = solver.Objective()
+    objetivo.SetMinimization()
+    for var in s.values(): objetivo.SetCoefficient(var, 1e8)
+    for (m, e, l_dec, l_pou, t), var in x.items():
+        objetivo.SetCoefficient(var, route_info[(m, e, l_dec, l_pou)]['combustivel_missao'])
+        
+    status = solver.Solve()
+    
+    print(f"\n--- SCIP FINISHED ---")
+    if status in [pywraplp.Solver.OPTIMAL, pywraplp.Solver.FEASIBLE]:
+        obj_val = solver.Objective().Value()
+        try:
+            best_bound = solver.Objective().BestBound()
+        except:
+            best_bound = obj_val
+        gap = abs((obj_val - best_bound) / obj_val) * 100 if obj_val > 0 else 0
+        
+        print(f"Final Status: {'OPTIMAL' if status == pywraplp.Solver.OPTIMAL else 'FEASIBLE (Time Limit)'}")
+        print(f"Optimized Cost: {obj_val:,.2f} L")
+        with open(COST_FILE, "w") as f:
+            f.write(str(obj_val))
+        print(f"Dual Bound: {best_bound:,.2f} L")
+        print(f"Gap: {gap:.4f}%")
+        
+        results = []
+        for (m, e, l_dec, l_pou, t), var in x.items():
+            if var.solution_value() > 0:
+                results.append({
+                    'Data': t, 'Modelo': m, 'Emissor': e,
+                    'Local Decolagem': l_dec, 'Local Pouso': l_pou,
+                    'Qtd Aeronaves': int(var.solution_value()),
+                    'Pernoites': route_info[(m, e, l_dec, l_pou)]['num_pernoites']
+                })
+        df_results = pd.DataFrame(results)
+        df_results.to_csv(RESULTS_FILE, sep=';', index=False)
+        print(f"Results successfully saved to {RESULTS_FILE}")
+    else:
+        print("\n[ERROR] Solver failed to find a solution.")
+
+if __name__ == '__main__':
+    time_limit = int(sys.argv[1]) if len(sys.argv) > 1 else 300
+    run_solver(time_limit)

uv.lock

@@ -19,6 +19,22 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750, upload-time = "2026-01-28T10:17:04.19Z" },
 ]
 
+[[package]]
+name = "altair"
+version = "6.2.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "jinja2" },
+    { name = "jsonschema" },
+    { name = "narwhals" },
+    { name = "packaging" },
+    { name = "typing-extensions", marker = "python_full_version < '3.15'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/86/97/9a0dc61efd4f2dee29cb6d8edbbacdb789ce48cbffd98efa2b3ab145b297/altair-6.2.1.tar.gz", hash = "sha256:ca0298fa20b1a4fae22eff8847b95f74912bd90544013ad36af192119883ea64", size = 766468, upload-time = "2026-06-05T16:23:36.57Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/dc/78/b556548d92b9e29ae68a86e7b416888820900809e189e39caf308c7d44a3/altair-6.2.1-py3-none-any.whl", hash = "sha256:bf2fee3733c3a31a588e45b857a2495a88d506970deb87f74e1613f0247446b1", size = 797498, upload-time = "2026-06-05T16:23:34.799Z" },
+]
+
 [[package]]
 name = "anyio"
 version = "4.13.0"
@@ -163,6 +179,24 @@ css = [
     { name = "tinycss2" },
 ]
 
+[[package]]
+name = "blinker"
+version = "1.9.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/21/28/9b3f50ce0e048515135495f198351908d99540d69bfdc8c1d15b73dc55ce/blinker-1.9.0.tar.gz", hash = "sha256:b4ce2265a7abece45e7cc896e98dbebe6cead56bcf805a3d23136d145f5445bf", size = 22460, upload-time = "2024-11-08T17:25:47.436Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/10/cb/f2ad4230dc2eb1a74edf38f1a38b9b52277f75bef262d8908e60d957e13c/blinker-1.9.0-py3-none-any.whl", hash = "sha256:ba0efaa9080b619ff2f3459d1d500c57bddea4a6b424b60a91141db6fd2f08bc", size = 8458, upload-time = "2024-11-08T17:25:46.184Z" },
+]
+
+[[package]]
+name = "cachetools"
+version = "7.1.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f4/8b/0d3945a13955303b81272f759a0331e54c5c793da455e6f5706b89d2639c/cachetools-7.1.4.tar.gz", hash = "sha256:437f55a4e0c1b01a4f3077cc470e6991d47430970e36fbcb77e2be0df4fc1cd6", size = 40085, upload-time = "2026-05-21T22:40:43.376Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8c/7b/1fc1c09cc0756cf25861a3be10565915953876da48bb228fb9a672b20a42/cachetools-7.1.4-py3-none-any.whl", hash = "sha256:323dc4127934744db5b54eb4924482d7edafbf9554e820d1531c2e08c0e4ef54", size = 16761, upload-time = "2026-05-21T22:40:41.845Z" },
+]
+
 [[package]]
 name = "ceao-809"
 version = "0.1.0"
@@ -176,6 +210,8 @@ dependencies = [
     { name = "pulp" },
     { name = "pymap3d" },
     { name = "seaborn" },
+    { name = "streamlit" },
+    { name = "tabulate" },
     { name = "vincenty" },
 ]
 
@@ -189,6 +225,8 @@ requires-dist = [
     { name = "pulp", specifier = ">=3.3.2" },
     { name = "pymap3d", specifier = ">=3.2.0" },
     { name = "seaborn", specifier = ">=0.13.2" },
+    { name = "streamlit", specifier = ">=1.58.0" },
+    { name = "tabulate", specifier = ">=0.10.0" },
     { name = "vincenty", specifier = ">=0.1.4" },
 ]
 
@@ -331,6 +369,18 @@ wheels = [
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 ]
 
+[[package]]
+name = "click"
+version = "8.4.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9b/98/518d8e5081007684232226f475082b30087d0f585e8457db087298259f49/click-8.4.1.tar.gz", hash = "sha256:918b5633eddf6b41c32d4f454bf0de810065c74e3f7dbf8ee5452f8be88d3e96", size = 353007, upload-time = "2026-05-22T04:08:37.769Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c7/0d/67e5b4109ea4a837e80daa87c2c696711955e40449a97e8926672534def2/click-8.4.1-py3-none-any.whl", hash = "sha256:482be17c6991b8c19c5429a1e995d9b0efdbb63172824c41f99965dc0ade8ec2", size = 116639, upload-time = "2026-05-22T04:08:35.26Z" },
+]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -531,6 +581,30 @@ wheels = [
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 ]
 
+[[package]]
+name = "gitdb"
+version = "4.0.12"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "smmap" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/72/94/63b0fc47eb32792c7ba1fe1b694daec9a63620db1e313033d18140c2320a/gitdb-4.0.12.tar.gz", hash = "sha256:5ef71f855d191a3326fcfbc0d5da835f26b13fbcba60c32c21091c349ffdb571", size = 394684, upload-time = "2025-01-02T07:20:46.413Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a0/61/5c78b91c3143ed5c14207f463aecfc8f9dbb5092fb2869baf37c273b2705/gitdb-4.0.12-py3-none-any.whl", hash = "sha256:67073e15955400952c6565cc3e707c554a4eea2e428946f7a4c162fab9bd9bcf", size = 62794, upload-time = "2025-01-02T07:20:43.624Z" },
+]
+
+[[package]]
+name = "gitpython"
+version = "3.1.50"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "gitdb" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/33/f6/354ae6491228b5eb40e10d89c4d13c651fe1cf7556e35ebdded50cff57ce/gitpython-3.1.50.tar.gz", hash = "sha256:80da2d12504d52e1f998772dc5baf6e553f8d2fcfe1fcc226c9d9a2ee3372dcc", size = 219798, upload-time = "2026-05-06T04:01:26.571Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/20/7a/1c6e3562dfd8950adbb11ffbc65d21e7c89d01a6e4f137fa981056de25c5/gitpython-3.1.50-py3-none-any.whl", hash = "sha256:d352abe2908d07355014abdd21ddf798c2a961469239afec4962e9da884858f9", size = 212507, upload-time = "2026-05-06T04:01:23.799Z" },
+]
+
 [[package]]
 name = "h11"
 version = "0.16.0"
@@ -553,6 +627,42 @@ wheels = [
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+[[package]]
+name = "httptools"
+version = "0.8.0"
+source = { registry = "https://pypi.org/simple" }
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