3607965c88
- 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>
233 lines
9.8 KiB
Python
233 lines
9.8 KiB
Python
"""
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Aircraft Routing Dashboard – Streamlit application.
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Run with: streamlit run app/dashboard.py (from project root)
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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# Allow imports from src/ when running from project root
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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import streamlit as st
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import pandas as pd
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import plotly.express as px
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import plotly.graph_objects as go
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from datetime import timedelta
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from src.routing_engine import RoutingPipeline, DEFAULT_CONFIG
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# ── Page config ───────────────────────────────────────────────────────────────
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st.set_page_config(
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page_title="OARMP – Aircraft Routing",
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page_icon="✈",
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layout="wide",
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)
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st.title("✈ Aircraft Routing & Maintenance Planning (OARMP)")
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st.caption("Set Partitioning + Column Generation + Branch & Bound")
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# ── Sidebar – parameters ──────────────────────────────────────────────────────
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with st.sidebar:
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st.header("⚙ Parameters")
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tat = st.number_input("TAT mínimo (min)", min_value=0, max_value=240, value=60, step=10)
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year = st.number_input("Ano do planejamento", min_value=2024, max_value=2030, value=2026)
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time_limit = st.number_input("Limite tempo solver (s)", min_value=30, max_value=600, value=120)
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run_btn = st.button("▶ Executar Otimização", type="primary")
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# ── Session state ─────────────────────────────────────────────────────────────
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if "result" not in st.session_state:
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st.session_state["result"] = None
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# ── Run pipeline ──────────────────────────────────────────────────────────────
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if run_btn:
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cfg = DEFAULT_CONFIG
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cfg.tat_minutes = tat
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cfg.planning_year = int(year)
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cfg.mip_time_limit_seconds = int(time_limit)
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with st.spinner("Executando otimização…"):
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try:
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pipe = RoutingPipeline(cfg)
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st.session_state["result"] = pipe.run(save_outputs=True)
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st.success("Otimização concluída!")
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except Exception as exc:
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st.error(f"Erro: {exc}")
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st.exception(exc)
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result = st.session_state.get("result")
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# ── Tabs ──────────────────────────────────────────────────────────────────────
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tab_summary, tab_gantt, tab_maint, tab_fleet, tab_ofrags = st.tabs(
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["📊 Resumo", "📅 Gantt", "🔧 Manutenção", "✈ Frota", "📋 OFRAGs"]
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)
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# ── Tab: Summary ──────────────────────────────────────────────────────────────
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with tab_summary:
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if result is None:
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st.info("Execute a otimização para ver os resultados.")
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else:
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s = result["summary"]
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c1, c2, c3, c4 = st.columns(4)
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c1.metric("Status", s["status"])
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c2.metric("Objetivo (FH perdidas)", f"{s['total_ttm_loss_hours']:.2f} h")
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c3.metric("OFRAGs cobertas", f"{s['covered_ofrags']} / {s['total_ofrags']}")
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c4.metric("Eventos de manutenção", s["n_maintenance_events"])
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if s["uncovered_ofrags"]:
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st.warning(f"OFRAGs NÃO cobertas: {s['uncovered_ofrags']}")
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st.subheader("Escala de voo otimizada")
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sched = result["schedule"]
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if not sched.empty:
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st.dataframe(
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sched.style.apply(
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lambda r: ["background-color:#fff3cd" if r.get("maintenance_before") else "" for _ in r],
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axis=1,
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),
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use_container_width=True,
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)
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st.caption(f"Colunas geradas no CG: {s['columns_generated']}")
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# ── Tab: Gantt ────────────────────────────────────────────────────────────────
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with tab_gantt:
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if result is None:
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st.info("Execute a otimização para ver o diagrama de Gantt.")
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else:
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sched = result["schedule"]
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maint = result["maintenance"]
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gantt_rows = []
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if not sched.empty:
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for _, row in sched.iterrows():
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if pd.isna(row.get("departure")) or pd.isna(row.get("arrival")):
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continue
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gantt_rows.append(
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dict(
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Task=row["aircraft"],
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Start=row["departure"],
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Finish=row["arrival"],
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Resource=row["ofrag_id"],
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Type="OFRAG",
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Label=row["ofrag_id"],
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)
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)
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if not maint.empty:
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for _, row in maint.iterrows():
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if pd.isna(row.get("maint_start")) or pd.isna(row.get("maint_end")):
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continue
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gantt_rows.append(
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dict(
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Task=row["aircraft"],
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Start=row["maint_start"],
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Finish=row["maint_end"],
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Resource="Manutenção",
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Type="Maintenance",
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Label=f"CHECK (perde {row['ttm_loss_hours']:.1f}h)",
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)
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)
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if gantt_rows:
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df_gantt = pd.DataFrame(gantt_rows)
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color_map = {"Manutenção": "#636EFA"}
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fig = px.timeline(
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df_gantt,
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x_start="Start",
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x_end="Finish",
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y="Task",
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color="Type",
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text="Label",
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title="Escala de Voo e Manutenção por Aeronave",
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color_discrete_map={"OFRAG": "#00CC96", "Maintenance": "#EF553B"},
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)
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fig.update_yaxes(categoryorder="category ascending")
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fig.update_traces(textposition="inside")
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fig.update_layout(height=400 + len(sched["aircraft"].unique()) * 40)
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st.plotly_chart(fig, use_container_width=True)
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cfg_obj = DEFAULT_CONFIG
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try:
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fig.write_html(str(cfg_obj.figures_dir / "gantt.html"))
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except Exception:
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pass
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else:
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st.info("Nenhum dado de Gantt disponível.")
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# ── Tab: Maintenance ──────────────────────────────────────────────────────────
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with tab_maint:
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if result is None:
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st.info("Execute a otimização para ver eventos de manutenção.")
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else:
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maint = result["maintenance"]
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if maint.empty:
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st.success("Nenhum evento de manutenção forçado.")
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else:
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st.dataframe(maint, use_container_width=True)
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fig_loss = px.bar(
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maint,
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x="aircraft",
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y="ttm_loss_hours",
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color="check_cycle_index",
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title="Horas de TTM perdidas por aeronave / ciclo de check",
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labels={"ttm_loss_hours": "TTM perdido (h)", "aircraft": "Aeronave"},
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text_auto=".2f",
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)
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st.plotly_chart(fig_loss, use_container_width=True)
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# ── Tab: Fleet ────────────────────────────────────────────────────────────────
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with tab_fleet:
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if result is None:
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st.info("Execute a otimização.")
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else:
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fleet_df = result["fleet"]
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st.dataframe(fleet_df, use_container_width=True)
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fig_util = px.bar(
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fleet_df,
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x="aircraft",
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y="ttm_utilisation_pct",
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title="Utilização do TTM por aeronave (%)",
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labels={"ttm_utilisation_pct": "Utilização TTM (%)", "aircraft": "Aeronave"},
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text_auto=".1f",
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color="ttm_utilisation_pct",
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color_continuous_scale="RdYlGn",
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range_color=[0, 100],
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)
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st.plotly_chart(fig_util, use_container_width=True)
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fig_fh = px.bar(
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fleet_df,
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x="aircraft",
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y=["initial_ttm_h", "flight_hours_scheduled", "total_ttm_loss_h"],
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barmode="group",
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title="TTM inicial vs FH planejadas vs FH perdidas",
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labels={"value": "Horas de voo", "aircraft": "Aeronave"},
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)
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st.plotly_chart(fig_fh, use_container_width=True)
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# ── Tab: OFRAGs ───────────────────────────────────────────────────────────────
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with tab_ofrags:
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if result is None:
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st.info("Execute a otimização.")
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else:
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ofrags = result["ofrags"]
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st.write(f"**{len(ofrags)} OFRAGs** elegíveis (iniciam e terminam na base de manutenção).")
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cols_show = [c for c in ["ofrag_id", "departure", "arrival", "flight_hours", "n_legs", "missions", "origin", "destination"] if c in ofrags.columns]
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st.dataframe(ofrags[cols_show], use_container_width=True)
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fig_fh = px.bar(
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ofrags,
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x="ofrag_id",
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y="flight_hours",
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title="Horas de voo por OFRAG",
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labels={"flight_hours": "FH total", "ofrag_id": "OFRAG"},
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text_auto=".2f",
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)
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st.plotly_chart(fig_fh, use_container_width=True)
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