fleet-assignment/modelos.ipynb

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    "# Alocação de Frotas (Fleet Scheduling) - Aeronaves C-97\n",
    "\n",
    "Este projeto visa realizar a alocação de frota (Fleet Assignment) baseada nos princípios do livro referenciado *Airline Operations and Scheduling*.\n",
    "Diferentemente do modelo anterior que utilizava aeronaves de carga, este foca exclusivamente na aeronave de passageiros **C-97**.\n",
    "\n",
    "## Objetivos:\n",
    "1. Remover cálculos de aeronaves de carga.\n",
    "2. Analisar as demandas diárias para a aeronave C-97.\n",
    "3. Identificar os esquadrões detentores dessas aeronaves e a quantidade de aeronaves (matrículas únicas).\n",
    "4. Calcular os 5 trechos mais relevantes do ano de 2025 utilizando estatística e gráficos.\n",
    "5. Realizar o Fleet Assignment para os 5 trechos, visando minimizar o custo total da operação (Modelo 1 proposto).\n"
   ]
  },
  {
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   "source": [
    "# Importação de bibliotecas\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pulp\n",
    "import pymap3d as pm\n",
    "import math\n",
    "import networkx as nx\n",
    "\n",
    "# Configuração de estilo para os gráficos\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# Constantes globais\n",
    "CSV_FILEPATH = \"SISCO_AEREA_01-01-2025_A_31-12-2025_1911251714Z.csv\"\n",
    "JSON_AEROPORTOS = \"airports.json\"\n",
    "CAPACIDADE_C97 = 30 # Capacidade média de passageiros do C-97 (Brasília)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "804eaef2",
   "metadata": {},
   "source": [
    "## 1. Leitura e Limpeza dos Dados\n",
    "Vamos carregar os dados, filtrar apenas para o modelo `C-97` e tratar os campos de localidades e datas.\n"
   ]
  },
  {
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   "execution_count": 2,
   "id": "dec4d110",
   "metadata": {
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   "source": [
    "# Carregamento do banco de dados\n",
    "df = pd.read_csv(CSV_FILEPATH, low_memory=False)\n",
    "df_aeroportos = pd.read_json(JSON_AEROPORTOS, orient='index')\n",
    "\n",
    "# Tratamento básico de valores nulos expressos como 'NIL'\n",
    "df.replace('NIL', 0, inplace=True)\n",
    "\n",
    "# Filtro para a aeronave C-97 (Passageiros) e remoção de registros de carga\n",
    "df_c97 = df[df['Modelo'] == 'C-97'].copy()\n",
    "\n",
    "# Filtrando decolagens e pousos conhecidos\n",
    "df_c97 = df_c97[df_c97['Localidade Decolagem'] != 0]\n",
    "df_c97 = df_c97[df_c97['Localidade Pouso'] != 0]\n",
    "\n",
    "# Criação da coluna Trecho\n",
    "df_c97['Trecho'] = df_c97['Localidade Decolagem'] + '-' + df_c97['Localidade Pouso']\n",
    "\n",
    "# Conversão da coluna PAX para numérico\n",
    "df_c97['PAX'] = pd.to_numeric(df_c97['PAX']).fillna(0)\n",
    "\n",
    "# Tratamento de datas\n",
    "df_c97['Dep TimeStamp'] = pd.to_datetime(df_c97['Data de Decolagem'] + ' ' + df_c97['Hora de Decolagem'], format='mixed', dayfirst=True)\n",
    "df_c97['Data Apenas'] = df_c97['Dep TimeStamp'].dt.date\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68fc01d6",
   "metadata": {},
   "source": [
    "## 2. Análise dos Esquadrões e Matrículas (Aeronaves)\n",
    "Identificaremos quais esquadrões operam o C-97 e qual a disponibilidade de aeronaves (número de matrículas únicas), pois isso definirá as restrições de frota.\n"
   ]
  },
  {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Esquadrões que operam o C-97: ETA1, ETA3, ETA5, ETA6, ETA7, ETA2, PAMA SP\n",
      "Total de matrículas únicas (aeronaves disponíveis): 14\n",
      "\n",
      "Aeronaves por esquadrão:\n",
      " - ETA1: 1 aeronaves\n",
      " - ETA2: 1 aeronaves\n",
      " - ETA3: 3 aeronaves\n",
      " - ETA5: 1 aeronaves\n",
      " - ETA6: 4 aeronaves\n",
      " - ETA7: 3 aeronaves\n",
      " - PAMA SP: 1 aeronaves\n",
      "\n",
      "Bases inferidas por esquadrão (pela moda de decolagens):\n",
      " - ETA1: SBBE\n",
      " - ETA2: SBNT\n",
      " - ETA3: SBGL\n",
      " - ETA5: SBCO\n",
      " - ETA6: SBBR\n",
      " - ETA7: SBMN\n",
      " - PAMA SP: SBGR\n"
     ]
    }
   ],
   "source": [
    "# Obtendo esquadrões e matrículas únicas\n",
    "esquadroes = df_c97['Emissor'].unique()\n",
    "matriculas_unicas = df_c97['Matrícula'].unique()\n",
    "total_aeronaves = len(matriculas_unicas)\n",
    "\n",
    "print(f\"Esquadrões que operam o C-97: {', '.join(esquadroes)}\")\n",
    "print(f\"Total de matrículas únicas (aeronaves disponíveis): {total_aeronaves}\")\n",
    "\n",
    "# Aeronaves por esquadrão\n",
    "aeronaves_por_esquadrao = df_c97.groupby('Emissor')['Matrícula'].nunique().to_dict()\n",
    "print(\"\\nAeronaves por esquadrão:\")\n",
    "for esq, qtd in aeronaves_por_esquadrao.items():\n",
    "    print(f\" - {esq}: {qtd} aeronaves\")\n",
    "    \n",
    "# Identificar a base principal de cada esquadrão (aeroporto com mais decolagens)\n",
    "bases_esquadroes = {}\n",
    "for esq in aeronaves_por_esquadrao.keys():\n",
    "    base = df_c97[df_c97['Emissor'] == esq]['Localidade Decolagem'].mode()[0]\n",
    "    bases_esquadroes[esq] = base\n",
    "\n",
    "print(\"\\nBases inferidas por esquadrão (pela moda de decolagens):\")\n",
    "for esq, base in bases_esquadroes.items():\n",
    "    print(f\" - {esq}: {base}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "875b834d",
   "metadata": {},
   "source": [
    "## 3. Análise Estatística das Demandas Diárias\n",
    "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.\n",
    "Começaremos focando em 5 trechos (alvo variável).\n"
   ]
  },
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       "      <th></th>\n",
       "      <th>Trecho</th>\n",
       "      <th>Media_PAX_Diario</th>\n",
       "      <th>Desvio_Padrao_PAX</th>\n",
       "      <th>Total_PAX</th>\n",
       "      <th>Dias_Operados</th>\n",
       "      <th>Media_PAX_Dias_Operados</th>\n",
       "      <th>Score_Relevancia</th>\n",
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       "      <th>223</th>\n",
       "      <td>SBGL-SBBR</td>\n",
       "      <td>0.980822</td>\n",
       "      <td>4.097998</td>\n",
       "      <td>358.0</td>\n",
       "      <td>38</td>\n",
       "      <td>9.421053</td>\n",
       "      <td>13604.0</td>\n",
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       "      <td>SBBR-SBGL</td>\n",
       "      <td>0.794521</td>\n",
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       "      <td>290.0</td>\n",
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       "      <td>0.487671</td>\n",
       "      <td>2.420831</td>\n",
       "      <td>178.0</td>\n",
       "      <td>21</td>\n",
       "      <td>8.476190</td>\n",
       "      <td>3738.0</td>\n",
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       "      <td>SBGR-SBBR</td>\n",
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       "      <td>174.0</td>\n",
       "      <td>21</td>\n",
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       "      <td>SBGL-SBGR</td>\n",
       "      <td>0.460274</td>\n",
       "      <td>2.575601</td>\n",
       "      <td>168.0</td>\n",
       "      <td>19</td>\n",
       "      <td>8.842105</td>\n",
       "      <td>3192.0</td>\n",
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       "        Trecho  Media_PAX_Diario  Desvio_Padrao_PAX  Total_PAX  Dias_Operados  \\\n",
       "223  SBGL-SBBR          0.980822           4.097998      358.0             38   \n",
       "69   SBBR-SBGL          0.794521           3.483710      290.0             34   \n",
       "72   SBBR-SBGR          0.487671           2.420831      178.0             21   \n",
       "272  SBGR-SBBR          0.476712           2.481277      174.0             21   \n",
       "237  SBGL-SBGR          0.460274           2.575601      168.0             19   \n",
       "\n",
       "     Media_PAX_Dias_Operados  Score_Relevancia  \n",
       "223                 9.421053           13604.0  \n",
       "69                  8.529412            9860.0  \n",
       "72                  8.476190            3738.0  \n",
       "272                 8.285714            3654.0  \n",
       "237                 8.842105            3192.0  "
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Demanda diária por trecho\n",
    "demanda_diaria = df_c97.groupby(['Trecho', 'Data Apenas'])['PAX'].sum().reset_index()\n",
    "\n",
    "# Criar um range de datas para todo o ano de 2025 para contabilizar os dias sem voo\n",
    "import itertools\n",
    "datas_2025 = pd.date_range(start='2025-01-01', end='2025-12-31').date\n",
    "todos_trechos = demanda_diaria['Trecho'].unique()\n",
    "idx = pd.MultiIndex.from_product([todos_trechos, datas_2025], names=['Trecho', 'Data Apenas'])\n",
    "demanda_diaria_completa = pd.DataFrame(index=idx).reset_index()\n",
    "\n",
    "# Mesclar com os dados reais e preencher NaN com 0\n",
    "demanda_diaria_completa = pd.merge(demanda_diaria_completa, demanda_diaria, on=['Trecho', 'Data Apenas'], how='left').fillna({'PAX': 0})\n",
    "\n",
    "# Estatísticas (Média e Desvio Padrão) por trecho\n",
    "estatisticas_trechos = demanda_diaria_completa.groupby('Trecho').agg(\n",
    "    Media_PAX_Diario=('PAX', 'mean'),\n",
    "    Desvio_Padrao_PAX=('PAX', 'std'),\n",
    "    Total_PAX=('PAX', 'sum'),\n",
    "    Dias_Operados=('PAX', lambda x: (x > 0).sum())\n",
    ").reset_index()\n",
    "\n",
    "# Média PAX por dias operados (usado para o Fleet Assignment Model)\n",
    "estatisticas_trechos['Media_PAX_Dias_Operados'] = estatisticas_trechos.apply(\n",
    "    lambda row: row['Total_PAX'] / row['Dias_Operados'] if row['Dias_Operados'] > 0 else 0, axis=1\n",
    ")\n",
    "\n",
    "# Nova métrica solicitada: PAX * Dias_Operados\n",
    "estatisticas_trechos['Score_Relevancia'] = estatisticas_trechos['Total_PAX'] * estatisticas_trechos['Dias_Operados']\n",
    "\n",
    "# Ordenar pelas rotas com maior métrica (PAX * Dias_Operados)\n",
    "estatisticas_trechos = estatisticas_trechos.sort_values(by='Score_Relevancia', ascending=False)\n",
    "\n",
    "# Definir número de trechos alvo\n",
    "NUM_TRECHOS = 5\n",
    "top_trechos = estatisticas_trechos.head(NUM_TRECHOS)\n",
    "\n",
    "display(top_trechos)\n",
    "\n",
    "# Gráfico\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(data=top_trechos, x='Trecho', y='Score_Relevancia', hue='Trecho', palette='viridis', legend=False)\n",
    "plt.title(f'Top {NUM_TRECHOS} Trechos Mais Relevantes (PAX * Dias Operados)')\n",
    "plt.ylabel('Score de Relevância (PAX * Dias Operados)')\n",
    "plt.xlabel('Trecho (Origem - Destino)')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96d89f3d",
   "metadata": {},
   "source": [
    "## Diagrama de Rede dos Trechos\n",
    "Para melhor visualização dos trechos mais relevantes, abaixo temos um diagrama de rede conectando as origens aos destinos.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b176401f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-07T23:24:42.006034Z",
     "iopub.status.busy": "2026-06-07T23:24:42.005909Z",
     "iopub.status.idle": "2026-06-07T23:24:42.099801Z",
     "shell.execute_reply": "2026-06-07T23:24:42.099424Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Criação do grafo direcionado\n",
    "G = nx.DiGraph()\n",
    "\n",
    "# Adiciona arestas com peso baseado no Score de Relevância\n",
    "for _, row in top_trechos.iterrows():\n",
    "    origem, destino = row['Trecho'].split('-')\n",
    "    peso = row['Score_Relevancia']\n",
    "    G.add_edge(origem, destino, weight=peso)\n",
    "\n",
    "# Configuração do layout do grafo\n",
    "pos = nx.spring_layout(G, seed=42)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "# Desenha os nós\n",
    "nx.draw_networkx_nodes(G, pos, node_size=2000, node_color='skyblue', edgecolors='black')\n",
    "# Desenha as arestas\n",
    "nx.draw_networkx_edges(G, pos, edgelist=G.edges(), arrowstyle='-|>', arrowsize=20, edge_color='gray', width=2, connectionstyle='arc3,rad=0.1')\n",
    "# Desenha os rótulos (nomes dos aeroportos)\n",
    "nx.draw_networkx_labels(G, pos, font_size=12, font_family='sans-serif', font_weight='bold')\n",
    "\n",
    "plt.title('Diagrama de Rede - Top Trechos Relevantes', fontsize=14)\n",
    "plt.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9af54886",
   "metadata": {},
   "source": [
    "## 4. Fleet Assignment Model (FAM)\n",
    "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.\n",
    "\n",
    "**Modelo Matemático:**\n",
    "- **Variáveis de decisão:** $X_{s, r}$ (Quantidade de voos operados pelo esquadrão $s$ no trecho $r$)\n",
    "- **Função Objetivo:** Minimizar a distância total (Distância de posicionamento da base + Distância do trecho + Retorno para a base).\n",
    "- **Restrições:**\n",
    "  1. O número de voos alocados para o trecho deve suprir a demanda média (Média PAX / Capacidade do C-97).\n",
    "  2. O total de voos atribuídos a um esquadrão não pode exceder o número de aeronaves disponíveis naquele esquadrão no dia.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "05dc0200",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-07T23:24:42.101381Z",
     "iopub.status.busy": "2026-06-07T23:24:42.101256Z",
     "iopub.status.idle": "2026-06-07T23:24:42.158882Z",
     "shell.execute_reply": "2026-06-07T23:24:42.158241Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== RESULTADO DO FLEET ASSIGNMENT ==\n",
      "Status: Optimal\n",
      "Distância Total Minimizada: 7722.27 km\n",
      "\n",
      "Alocações (Voos Diários):\n",
      "\n",
      "Trecho: SBGL-SBBR (Voos necessários: 1)\n",
      " -> ETA3 (Base SBGL): 1 voo(s)\n",
      "\n",
      "Trecho: SBBR-SBGL (Voos necessários: 1)\n",
      " -> ETA3 (Base SBGL): 1 voo(s)\n",
      "\n",
      "Trecho: SBBR-SBGR (Voos necessários: 1)\n",
      " -> ETA6 (Base SBBR): 1 voo(s)\n",
      "\n",
      "Trecho: SBGR-SBBR (Voos necessários: 1)\n",
      " -> ETA6 (Base SBBR): 1 voo(s)\n",
      "\n",
      "Trecho: SBGL-SBGR (Voos necessários: 1)\n",
      " -> ETA3 (Base SBGL): 1 voo(s)\n"
     ]
    }
   ],
   "source": [
    "# Preparação dos dados para o modelo\n",
    "rotas_lista = top_trechos['Trecho'].tolist()\n",
    "\n",
    "# Dicionário de distâncias\n",
    "def calc_distancia(icao1, icao2):\n",
    "    try:\n",
    "        lat1, lon1, alt1 = df_aeroportos.loc[icao1, ['lat', 'lon', 'elevation']]\n",
    "        lat2, lon2, alt2 = df_aeroportos.loc[icao2, ['lat', 'lon', 'elevation']]\n",
    "        # Retorna a distância oblíqua em km\n",
    "        return pm.geodetic2aer(lat1, lon1, alt1, lat2, lon2, alt2)[2] / 1000.0\n",
    "    except KeyError:\n",
    "        return 9999.0 # Penalidade caso aeroporto não exista no json\n",
    "\n",
    "distancias_voo = {}\n",
    "for r in rotas_lista:\n",
    "    origem, destino = r.split('-')\n",
    "    dist_trecho = calc_distancia(origem, destino)\n",
    "    for esq, base in bases_esquadroes.items():\n",
    "        dist_ida = calc_distancia(base, origem) if base != origem else 0\n",
    "        dist_volta = calc_distancia(destino, base) if base != destino else 0\n",
    "        distancias_voo[(esq, r)] = dist_ida + dist_trecho + dist_volta\n",
    "\n",
    "# Demanda de voos por rota (Média diária)\n",
    "voos_requeridos = {}\n",
    "for _, row in top_trechos.iterrows():\n",
    "    # Arredondamento para cima da (demanda / capacidade)\n",
    "    voos = math.ceil(row['Media_PAX_Dias_Operados'] / CAPACIDADE_C97)\n",
    "    # Pelo menos 1 voo para suprir a demanda da rota selecionada\n",
    "    voos_requeridos[row['Trecho']] = max(1, voos)\n",
    "\n",
    "# MODELAGEM COM PULP\n",
    "prob = pulp.LpProblem(\"Fleet_Assignment_C97\", pulp.LpMinimize)\n",
    "\n",
    "# Variáveis\n",
    "X = pulp.LpVariable.dicts(\"X\", [(esq, r) for esq in aeronaves_por_esquadrao.keys() for r in rotas_lista], lowBound=0, cat='Integer')\n",
    "\n",
    "# Função Objetivo\n",
    "prob += pulp.lpSum([distancias_voo[(esq, r)] * X[(esq, r)] for esq in aeronaves_por_esquadrao.keys() for r in rotas_lista])\n",
    "\n",
    "# Restrição 1: Suprir a demanda da rota\n",
    "for r in rotas_lista:\n",
    "    prob += pulp.lpSum([X[(esq, r)] for esq in aeronaves_por_esquadrao.keys()]) >= voos_requeridos[r]\n",
    "\n",
    "# Restrição 2: Limite de aeronaves por esquadrão\n",
    "for esq in aeronaves_por_esquadrao.keys():\n",
    "    prob += pulp.lpSum([X[(esq, r)] for r in rotas_lista]) <= aeronaves_por_esquadrao[esq]\n",
    "\n",
    "# Solução\n",
    "prob.solve(pulp.PULP_CBC_CMD(msg=False))\n",
    "\n",
    "# Exibição dos resultados\n",
    "print(\"== RESULTADO DO FLEET ASSIGNMENT ==\")\n",
    "print(f\"Status: {pulp.LpStatus[prob.status]}\")\n",
    "print(f\"Distância Total Minimizada: {pulp.value(prob.objective):.2f} km\\n\")\n",
    "\n",
    "print(\"Alocações (Voos Diários):\")\n",
    "for r in rotas_lista:\n",
    "    print(f\"\\nTrecho: {r} (Voos necessários: {voos_requeridos[r]})\")\n",
    "    for esq in aeronaves_por_esquadrao.keys():\n",
    "        qtd = int(X[(esq, r)].varValue)\n",
    "        if qtd > 0:\n",
    "            print(f\" -> {esq} (Base {bases_esquadroes[esq]}): {qtd} voo(s)\")\n"
   ]
  }
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