diff --git a/create_nb.py b/create_nb.py
index 04f8476..b46a105 100644
--- a/create_nb.py
+++ b/create_nb.py
@@ -127,8 +127,13 @@ estatisticas_trechos = demanda_diaria_completa.groupby('Trecho').agg(
     Dias_Operados=('PAX', lambda x: (x > 0).sum())
 ).reset_index()
 
-# Ordenar pelas rotas com maior número de dias operados
-estatisticas_trechos = estatisticas_trechos.sort_values(by='Dias_Operados', ascending=False)
+# Nova métrica: PAX/Dias_Operados (evitando divisão por zero)
+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
+)
+
+# Ordenar pelas rotas com maior Média PAX por dias operados
+estatisticas_trechos = estatisticas_trechos.sort_values(by='Media_PAX_Dias_Operados', ascending=False)
 
 # Definir número de trechos alvo
 NUM_TRECHOS = 5
@@ -138,9 +143,9 @@ display(top_trechos)
 
 # Gráfico
 plt.figure(figsize=(10, 6))
-sns.barplot(data=top_trechos, x='Trecho', y='Dias_Operados', hue='Trecho', palette='viridis', legend=False)
-plt.title(f'Top {NUM_TRECHOS} Trechos Mais Relevantes (Por Dias Operados)')
-plt.ylabel('Número de Dias Operados no Ano')
+sns.barplot(data=top_trechos, x='Trecho', y='Media_PAX_Dias_Operados', hue='Trecho', palette='viridis', legend=False)
+plt.title(f'Top {NUM_TRECHOS} Trechos Mais Relevantes (PAX / Dias Operados)')
+plt.ylabel('Média de PAX por Dia Operado')
 plt.xlabel('Trecho (Origem - Destino)')
 plt.xticks(rotation=45)
 plt.tight_layout()
@@ -186,7 +191,7 @@ for r in rotas_lista:
 voos_requeridos = {}
 for _, row in top_trechos.iterrows():
     # Arredondamento para cima da (demanda / capacidade)
-    voos = math.ceil(row['Media_PAX_Diario'] / CAPACIDADE_C97)
+    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)
 
diff --git a/modelos.ipynb b/modelos.ipynb
index a418d86..f6618e5 100644
--- a/modelos.ipynb
+++ b/modelos.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "4c8870e1",
+   "id": "490738bc",
    "metadata": {},
    "source": [
     "# Alocação de Frotas (Fleet Scheduling) - Aeronaves C-97\n",
@@ -21,13 +21,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "05ab3533",
+   "id": "14292d89",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-07T23:14:33.557540Z",
-     "iopub.status.busy": "2026-06-07T23:14:33.557102Z",
-     "iopub.status.idle": "2026-06-07T23:14:34.031487Z",
-     "shell.execute_reply": "2026-06-07T23:14:34.030774Z"
+     "iopub.execute_input": "2026-06-07T23:16:26.495905Z",
+     "iopub.status.busy": "2026-06-07T23:16:26.495251Z",
+     "iopub.status.idle": "2026-06-07T23:16:26.997172Z",
+     "shell.execute_reply": "2026-06-07T23:16:26.996519Z"
     }
    },
    "outputs": [],
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4fa3e096",
+   "id": "02401ef8",
    "metadata": {},
    "source": [
     "## 1. Leitura e Limpeza dos Dados\n",
@@ -62,13 +62,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "c3db41e8",
+   "id": "b18a653b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-07T23:14:34.033030Z",
-     "iopub.status.busy": "2026-06-07T23:14:34.032852Z",
-     "iopub.status.idle": "2026-06-07T23:14:34.384932Z",
-     "shell.execute_reply": "2026-06-07T23:14:34.384238Z"
+     "iopub.execute_input": "2026-06-07T23:16:26.998926Z",
+     "iopub.status.busy": "2026-06-07T23:16:26.998670Z",
+     "iopub.status.idle": "2026-06-07T23:16:27.370579Z",
+     "shell.execute_reply": "2026-06-07T23:16:27.369986Z"
     }
    },
    "outputs": [],
@@ -100,7 +100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb0597db",
+   "id": "259a9e7a",
    "metadata": {},
    "source": [
     "## 2. Análise dos Esquadrões e Matrículas (Aeronaves)\n",
@@ -110,13 +110,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "645a0fe0",
+   "id": "409555a8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-07T23:14:34.386323Z",
-     "iopub.status.busy": "2026-06-07T23:14:34.386204Z",
-     "iopub.status.idle": "2026-06-07T23:14:34.397293Z",
-     "shell.execute_reply": "2026-06-07T23:14:34.396780Z"
+     "iopub.execute_input": "2026-06-07T23:16:27.372062Z",
+     "iopub.status.busy": "2026-06-07T23:16:27.371936Z",
+     "iopub.status.idle": "2026-06-07T23:16:27.383786Z",
+     "shell.execute_reply": "2026-06-07T23:16:27.383217Z"
     }
    },
    "outputs": [
@@ -175,7 +175,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "064c1b5d",
+   "id": "f4b3847d",
    "metadata": {},
    "source": [
     "## 3. Análise Estatística das Demandas Diárias\n",
@@ -186,13 +186,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "41750d35",
+   "id": "07fc4526",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-07T23:14:34.398764Z",
-     "iopub.status.busy": "2026-06-07T23:14:34.398643Z",
-     "iopub.status.idle": "2026-06-07T23:14:34.668556Z",
-     "shell.execute_reply": "2026-06-07T23:14:34.667962Z"
+     "iopub.execute_input": "2026-06-07T23:16:27.385108Z",
+     "iopub.status.busy": "2026-06-07T23:16:27.384987Z",
+     "iopub.status.idle": "2026-06-07T23:16:27.656394Z",
+     "shell.execute_reply": "2026-06-07T23:16:27.655862Z"
     }
    },
    "outputs": [
@@ -222,60 +222,73 @@
        "      <th>Desvio_Padrao_PAX</th>\n",
        "      <th>Total_PAX</th>\n",
        "      <th>Dias_Operados</th>\n",
+       "      <th>Media_PAX_Dias_Operados</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <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",
+       "      <th>303</th>\n",
+       "      <td>SBJU-SBNT</td>\n",
+       "      <td>0.109589</td>\n",
+       "      <td>2.093696</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>40.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <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",
+       "      <th>149</th>\n",
+       "      <td>SBCJ-SBBE</td>\n",
+       "      <td>0.065753</td>\n",
+       "      <td>1.256217</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>24.0</td>\n",
        "    </tr>\n",
        "    <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",
+       "      <th>324</th>\n",
+       "      <td>SBMA-SBBR</td>\n",
+       "      <td>0.065753</td>\n",
+       "      <td>1.256217</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>24.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>72</th>\n",
-       "      <td>SBBR-SBGR</td>\n",
-       "      <td>0.487671</td>\n",
-       "      <td>2.420831</td>\n",
-       "      <td>178.0</td>\n",
-       "      <td>21</td>\n",
+       "      <th>300</th>\n",
+       "      <td>SBJI-SBVH</td>\n",
+       "      <td>0.057534</td>\n",
+       "      <td>1.099190</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>21.0</td>\n",
        "    </tr>\n",
        "    <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",
+       "      <th>540</th>\n",
+       "      <td>SBVH-SBBR</td>\n",
+       "      <td>0.057534</td>\n",
+       "      <td>1.099190</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>21.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "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",
-       "272  SBGR-SBBR          0.476712           2.481277      174.0             21\n",
-       "72   SBBR-SBGR          0.487671           2.420831      178.0             21\n",
-       "253  SBGL-SBSJ          0.263014           1.619550       96.0             20"
+       "        Trecho  Media_PAX_Diario  Desvio_Padrao_PAX  Total_PAX  Dias_Operados  \\\n",
+       "303  SBJU-SBNT          0.109589           2.093696       40.0              1   \n",
+       "149  SBCJ-SBBE          0.065753           1.256217       24.0              1   \n",
+       "324  SBMA-SBBR          0.065753           1.256217       24.0              1   \n",
+       "300  SBJI-SBVH          0.057534           1.099190       21.0              1   \n",
+       "540  SBVH-SBBR          0.057534           1.099190       21.0              1   \n",
+       "\n",
+       "     Media_PAX_Dias_Operados  \n",
+       "303                     40.0  \n",
+       "149                     24.0  \n",
+       "324                     24.0  \n",
+       "300                     21.0  \n",
+       "540                     21.0  "
       ]
      },
      "metadata": {},
@@ -283,7 +296,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -314,8 +327,13 @@
     "    Dias_Operados=('PAX', lambda x: (x > 0).sum())\n",
     ").reset_index()\n",
     "\n",
-    "# Ordenar pelas rotas com maior número de dias operados\n",
-    "estatisticas_trechos = estatisticas_trechos.sort_values(by='Dias_Operados', ascending=False)\n",
+    "# Nova métrica: PAX/Dias_Operados (evitando divisão por zero)\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",
+    "# Ordenar pelas rotas com maior Média PAX por dias operados\n",
+    "estatisticas_trechos = estatisticas_trechos.sort_values(by='Media_PAX_Dias_Operados', ascending=False)\n",
     "\n",
     "# Definir número de trechos alvo\n",
     "NUM_TRECHOS = 5\n",
@@ -325,9 +343,9 @@
     "\n",
     "# Gráfico\n",
     "plt.figure(figsize=(10, 6))\n",
-    "sns.barplot(data=top_trechos, x='Trecho', y='Dias_Operados', hue='Trecho', palette='viridis', legend=False)\n",
-    "plt.title(f'Top {NUM_TRECHOS} Trechos Mais Relevantes (Por Dias Operados)')\n",
-    "plt.ylabel('Número de Dias Operados no Ano')\n",
+    "sns.barplot(data=top_trechos, x='Trecho', y='Media_PAX_Dias_Operados', hue='Trecho', palette='viridis', legend=False)\n",
+    "plt.title(f'Top {NUM_TRECHOS} Trechos Mais Relevantes (PAX / Dias Operados)')\n",
+    "plt.ylabel('Média de PAX por Dia Operado')\n",
     "plt.xlabel('Trecho (Origem - Destino)')\n",
     "plt.xticks(rotation=45)\n",
     "plt.tight_layout()\n",
@@ -336,7 +354,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0cb78b22",
+   "id": "3b70d27a",
    "metadata": {},
    "source": [
     "## 4. Fleet Assignment Model (FAM)\n",
@@ -353,13 +371,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "5747cb28",
+   "id": "862dd955",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-07T23:14:34.670051Z",
-     "iopub.status.busy": "2026-06-07T23:14:34.669927Z",
-     "iopub.status.idle": "2026-06-07T23:14:34.764631Z",
-     "shell.execute_reply": "2026-06-07T23:14:34.763920Z"
+     "iopub.execute_input": "2026-06-07T23:16:27.657968Z",
+     "iopub.status.busy": "2026-06-07T23:16:27.657797Z",
+     "iopub.status.idle": "2026-06-07T23:16:27.763485Z",
+     "shell.execute_reply": "2026-06-07T23:16:27.762884Z"
     }
    },
    "outputs": [
@@ -369,24 +387,25 @@
      "text": [
       "== RESULTADO DO FLEET ASSIGNMENT ==\n",
       "Status: Optimal\n",
-      "Distância Total Minimizada: 7591.01 km\n",
+      "Distância Total Minimizada: 31712.50 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: SBGR-SBBR (Voos necessários: 1)\n",
+      "Trecho: SBJU-SBNT (Voos necessários: 2)\n",
+      " -> ETA2 (Base SBNT): 1 voo(s)\n",
       " -> ETA6 (Base SBBR): 1 voo(s)\n",
       "\n",
-      "Trecho: SBBR-SBGR (Voos necessários: 1)\n",
+      "Trecho: SBCJ-SBBE (Voos necessários: 1)\n",
+      " -> ETA1 (Base SBBE): 1 voo(s)\n",
+      "\n",
+      "Trecho: SBMA-SBBR (Voos necessários: 1)\n",
       " -> ETA6 (Base SBBR): 1 voo(s)\n",
       "\n",
-      "Trecho: SBGL-SBSJ (Voos necessários: 1)\n",
-      " -> ETA3 (Base SBGL): 1 voo(s)\n"
+      "Trecho: SBJI-SBVH (Voos necessários: 1)\n",
+      " -> ETA7 (Base SBMN): 1 voo(s)\n",
+      "\n",
+      "Trecho: SBVH-SBBR (Voos necessários: 1)\n",
+      " -> ETA6 (Base SBBR): 1 voo(s)\n"
      ]
     }
    ],
@@ -417,7 +436,7 @@
     "voos_requeridos = {}\n",
     "for _, row in top_trechos.iterrows():\n",
     "    # Arredondamento para cima da (demanda / capacidade)\n",
-    "    voos = math.ceil(row['Media_PAX_Diario'] / CAPACIDADE_C97)\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",