{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6be31f50",
   "metadata": {},
   "source": [
    "# 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f4b8c44d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-07T23:27:29.153892Z",
     "iopub.status.busy": "2026-06-07T23:27:29.153192Z",
     "iopub.status.idle": "2026-06-07T23:27:29.811580Z",
     "shell.execute_reply": "2026-06-07T23:27:29.810502Z"
    }
   },
   "outputs": [],
   "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": "4a3f8284",
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "43261510",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-07T23:27:29.813511Z",
     "iopub.status.busy": "2026-06-07T23:27:29.813233Z",
     "iopub.status.idle": "2026-06-07T23:27:30.183686Z",
     "shell.execute_reply": "2026-06-07T23:27:30.182986Z"
    }
   },
   "outputs": [],
   "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": "bffd4e0c",
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "40061c45",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-07T23:27:30.185144Z",
     "iopub.status.busy": "2026-06-07T23:27:30.185022Z",
     "iopub.status.idle": "2026-06-07T23:27:30.196292Z",
     "shell.execute_reply": "2026-06-07T23:27:30.195756Z"
    }
   },
   "outputs": [
    {
     "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": "94ec1687",
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e2bb9ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-07T23:27:30.197674Z",
     "iopub.status.busy": "2026-06-07T23:27:30.197555Z",
     "iopub.status.idle": "2026-06-07T23:27:30.501774Z",
     "shell.execute_reply": "2026-06-07T23:27:30.501194Z"
    }
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <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",
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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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       "      <th>69</th>\n",
       "      <td>SBBR-SBGL</td>\n",
       "      <td>0.794521</td>\n",
       "      <td>3.483710</td>\n",
       "      <td>290.0</td>\n",
       "      <td>34</td>\n",
       "      <td>8.529412</td>\n",
       "      <td>9860.0</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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       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>SBGR-SBBR</td>\n",
       "      <td>0.476712</td>\n",
       "      <td>2.481277</td>\n",
       "      <td>174.0</td>\n",
       "      <td>21</td>\n",
       "      <td>8.285714</td>\n",
       "      <td>3654.0</td>\n",
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       "    <tr>\n",
       "      <th>237</th>\n",
       "      <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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       "      <th>437</th>\n",
       "      <td>SBSJ-SBBR</td>\n",
       "      <td>0.339726</td>\n",
       "      <td>2.016330</td>\n",
       "      <td>124.0</td>\n",
       "      <td>18</td>\n",
       "      <td>6.888889</td>\n",
       "      <td>2232.0</td>\n",
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       "      <th>277</th>\n",
       "      <td>SBGR-SBGL</td>\n",
       "      <td>0.353425</td>\n",
       "      <td>1.885025</td>\n",
       "      <td>129.0</td>\n",
       "      <td>17</td>\n",
       "      <td>7.588235</td>\n",
       "      <td>2193.0</td>\n",
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       "      <th>441</th>\n",
       "      <td>SBSJ-SBGL</td>\n",
       "      <td>0.284932</td>\n",
       "      <td>1.705169</td>\n",
       "      <td>104.0</td>\n",
       "      <td>19</td>\n",
       "      <td>5.473684</td>\n",
       "      <td>1976.0</td>\n",
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       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>SBGL-SBSJ</td>\n",
       "      <td>0.263014</td>\n",
       "      <td>1.619550</td>\n",
       "      <td>96.0</td>\n",
       "      <td>20</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>1920.0</td>\n",
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       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>SBBR-SBAN</td>\n",
       "      <td>0.336986</td>\n",
       "      <td>2.304767</td>\n",
       "      <td>123.0</td>\n",
       "      <td>13</td>\n",
       "      <td>9.461538</td>\n",
       "      <td>1599.0</td>\n",
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      "text/plain": [
       "        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",
       "437  SBSJ-SBBR          0.339726           2.016330      124.0             18   \n",
       "277  SBGR-SBGL          0.353425           1.885025      129.0             17   \n",
       "441  SBSJ-SBGL          0.284932           1.705169      104.0             19   \n",
       "253  SBGL-SBSJ          0.263014           1.619550       96.0             20   \n",
       "53   SBBR-SBAN          0.336986           2.304767      123.0             13   \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  \n",
       "437                 6.888889            2232.0  \n",
       "277                 7.588235            2193.0  \n",
       "441                 5.473684            1976.0  \n",
       "253                 4.800000            1920.0  \n",
       "53                  9.461538            1599.0  "
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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 = 10\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": "d6f1f668",
   "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": "4929bba6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-07T23:27:30.503463Z",
     "iopub.status.busy": "2026-06-07T23:27:30.503337Z",
     "iopub.status.idle": "2026-06-07T23:27:30.631863Z",
     "shell.execute_reply": "2026-06-07T23:27:30.631298Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 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 - k maior afasta mais os nós\n",
    "pos = nx.spring_layout(G, k=2.5, iterations=100, seed=42)\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "# Define o tamanho do nó\n",
    "tamanho_no = 1200\n",
    "\n",
    "# Desenha os nós\n",
    "nx.draw_networkx_nodes(G, pos, node_size=tamanho_no, node_color='skyblue', edgecolors='black')\n",
    "# Desenha as arestas, informando o node_size para que a seta não fique escondida sob o nó\n",
    "nx.draw_networkx_edges(\n",
    "    G, pos, \n",
    "    edgelist=G.edges(), \n",
    "    arrows=True, \n",
    "    arrowstyle='-|>', \n",
    "    arrowsize=25, \n",
    "    edge_color='gray', \n",
    "    width=2, \n",
    "    node_size=tamanho_no, \n",
    "    connectionstyle='arc3,rad=0.15'\n",
    ")\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": "59991398",
   "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": "be919c7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-07T23:27:30.633209Z",
     "iopub.status.busy": "2026-06-07T23:27:30.633086Z",
     "iopub.status.idle": "2026-06-07T23:27:30.740716Z",
     "shell.execute_reply": "2026-06-07T23:27:30.739554Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== RESULTADO DO FLEET ASSIGNMENT ==\n",
      "Status: Optimal\n",
      "Distância Total Minimizada: 15903.36 km\n",
      "\n",
      "Alocações (Voos Diários):\n",
      "\n",
      "Trecho: SBGL-SBBR (Voos necessários: 1)\n",
      " -> ETA6 (Base SBBR): 1 voo(s)\n",
      "\n",
      "Trecho: SBBR-SBGL (Voos necessários: 1)\n",
      " -> ETA2 (Base SBNT): 1 voo(s)\n",
      "\n",
      "Trecho: SBBR-SBGR (Voos necessários: 1)\n",
      " -> ETA5 (Base SBCO): 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",
      "\n",
      "Trecho: SBSJ-SBBR (Voos necessários: 1)\n",
      " -> ETA6 (Base SBBR): 1 voo(s)\n",
      "\n",
      "Trecho: SBGR-SBGL (Voos necessários: 1)\n",
      " -> PAMA SP (Base SBGR): 1 voo(s)\n",
      "\n",
      "Trecho: SBSJ-SBGL (Voos necessários: 1)\n",
      " -> ETA3 (Base SBGL): 1 voo(s)\n",
      "\n",
      "Trecho: SBGL-SBSJ (Voos necessários: 1)\n",
      " -> ETA3 (Base SBGL): 1 voo(s)\n",
      "\n",
      "Trecho: SBBR-SBAN (Voos necessários: 1)\n",
      " -> ETA6 (Base SBBR): 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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