diff --git a/create_nb.py b/create_nb.py
index aa3b1f7..c1c17d2 100644
--- a/create_nb.py
+++ b/create_nb.py
@@ -109,17 +109,24 @@ Começaremos focando em 5 trechos (alvo variável).
 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.groupby('Trecho').agg(
+estatisticas_trechos = demanda_diaria_completa.groupby('Trecho').agg(
     Media_PAX_Diario=('PAX', 'mean'),
     Desvio_Padrao_PAX=('PAX', 'std'),
     Total_PAX=('PAX', 'sum'),
-    Dias_Operados=('Data Apenas', 'count')
+    Dias_Operados=('PAX', lambda x: (x > 0).sum())
 ).reset_index()
 
-# Preencher NaN no desvio padrão (caso operado apenas 1 dia) com 0
-estatisticas_trechos['Desvio_Padrao_PAX'] = estatisticas_trechos['Desvio_Padrao_PAX'].fillna(0)
-
 # Ordenar pelas rotas de maior média de PAX diário
 estatisticas_trechos = estatisticas_trechos.sort_values(by='Media_PAX_Diario', ascending=False)
 
diff --git a/modelos.ipynb b/modelos.ipynb
index 8c661e8..7765ed1 100644
--- a/modelos.ipynb
+++ b/modelos.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b9d53e09",
+   "id": "90710a92",
    "metadata": {},
    "source": [
     "# Alocação de Frotas (Fleet Scheduling) - Aeronaves C-97\n",
@@ -21,13 +21,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "3a2a17df",
+   "id": "5e2bab53",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-07T23:05:31.524243Z",
-     "iopub.status.busy": "2026-06-07T23:05:31.523594Z",
-     "iopub.status.idle": "2026-06-07T23:05:32.744349Z",
-     "shell.execute_reply": "2026-06-07T23:05:32.743866Z"
+     "iopub.execute_input": "2026-06-07T23:11:56.681238Z",
+     "iopub.status.busy": "2026-06-07T23:11:56.680938Z",
+     "iopub.status.idle": "2026-06-07T23:11:57.169135Z",
+     "shell.execute_reply": "2026-06-07T23:11:57.168256Z"
     }
    },
    "outputs": [],
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb6f4d66",
+   "id": "01b6d291",
    "metadata": {},
    "source": [
     "## 1. Leitura e Limpeza dos Dados\n",
@@ -62,13 +62,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "759d8610",
+   "id": "39011534",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-07T23:05:32.746076Z",
-     "iopub.status.busy": "2026-06-07T23:05:32.745895Z",
-     "iopub.status.idle": "2026-06-07T23:05:33.165670Z",
-     "shell.execute_reply": "2026-06-07T23:05:33.165060Z"
+     "iopub.execute_input": "2026-06-07T23:11:57.170725Z",
+     "iopub.status.busy": "2026-06-07T23:11:57.170545Z",
+     "iopub.status.idle": "2026-06-07T23:11:57.534804Z",
+     "shell.execute_reply": "2026-06-07T23:11:57.534327Z"
     }
    },
    "outputs": [],
@@ -100,7 +100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e0eb26f1",
+   "id": "7727e9ca",
    "metadata": {},
    "source": [
     "## 2. Análise dos Esquadrões e Matrículas (Aeronaves)\n",
@@ -110,13 +110,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "85f8d91d",
+   "id": "1e40e4de",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-07T23:05:33.167076Z",
-     "iopub.status.busy": "2026-06-07T23:05:33.166953Z",
-     "iopub.status.idle": "2026-06-07T23:05:33.178236Z",
-     "shell.execute_reply": "2026-06-07T23:05:33.177665Z"
+     "iopub.execute_input": "2026-06-07T23:11:57.536360Z",
+     "iopub.status.busy": "2026-06-07T23:11:57.536237Z",
+     "iopub.status.idle": "2026-06-07T23:11:57.547915Z",
+     "shell.execute_reply": "2026-06-07T23:11:57.547430Z"
     }
    },
    "outputs": [
@@ -175,7 +175,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b5ea583",
+   "id": "7cf32f02",
    "metadata": {},
    "source": [
     "## 3. Análise Estatística das Demandas Diárias\n",
@@ -186,13 +186,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "88e83a21",
+   "id": "6ac506ed",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-07T23:05:33.179611Z",
-     "iopub.status.busy": "2026-06-07T23:05:33.179488Z",
-     "iopub.status.idle": "2026-06-07T23:05:33.379629Z",
-     "shell.execute_reply": "2026-06-07T23:05:33.378953Z"
+     "iopub.execute_input": "2026-06-07T23:11:57.549463Z",
+     "iopub.status.busy": "2026-06-07T23:11:57.549345Z",
+     "iopub.status.idle": "2026-06-07T23:11:57.812722Z",
+     "shell.execute_reply": "2026-06-07T23:11:57.812187Z"
     }
    },
    "outputs": [
@@ -226,44 +226,44 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>324</th>\n",
-       "      <td>SBMA-SBBR</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>24</td>\n",
-       "      <td>1</td>\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",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>540</th>\n",
-       "      <td>SBVH-SBBR</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>0.000000</td>\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",
+       "    </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",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>300</th>\n",
-       "      <td>SBJI-SBVH</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>0.000000</td>\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>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>SBBE-SBMA</td>\n",
-       "      <td>19.5</td>\n",
-       "      <td>6.363961</td>\n",
-       "      <td>39</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>450</th>\n",
-       "      <td>SBSJ-SBVT</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>0.000000</td>\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>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -271,11 +271,11 @@
       ],
       "text/plain": [
        "        Trecho  Media_PAX_Diario  Desvio_Padrao_PAX  Total_PAX  Dias_Operados\n",
-       "324  SBMA-SBBR              24.0           0.000000         24              1\n",
-       "540  SBVH-SBBR              21.0           0.000000         21              1\n",
-       "300  SBJI-SBVH              21.0           0.000000         21              1\n",
-       "29   SBBE-SBMA              19.5           6.363961         39              2\n",
-       "450  SBSJ-SBVT              19.0           0.000000         19              1"
+       "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"
       ]
      },
      "metadata": {},
@@ -283,7 +283,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -296,17 +296,24 @@
     "# 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.groupby('Trecho').agg(\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=('Data Apenas', 'count')\n",
+    "    Dias_Operados=('PAX', lambda x: (x > 0).sum())\n",
     ").reset_index()\n",
     "\n",
-    "# Preencher NaN no desvio padrão (caso operado apenas 1 dia) com 0\n",
-    "estatisticas_trechos['Desvio_Padrao_PAX'] = estatisticas_trechos['Desvio_Padrao_PAX'].fillna(0)\n",
-    "\n",
     "# Ordenar pelas rotas de maior média de PAX diário\n",
     "estatisticas_trechos = estatisticas_trechos.sort_values(by='Media_PAX_Diario', ascending=False)\n",
     "\n",
@@ -329,7 +336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "287b32d3",
+   "id": "eadb21ce",
    "metadata": {},
    "source": [
     "## 4. Fleet Assignment Model (FAM)\n",
@@ -346,13 +353,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "4003d6aa",
+   "id": "c485ad06",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-07T23:05:33.381203Z",
-     "iopub.status.busy": "2026-06-07T23:05:33.381075Z",
-     "iopub.status.idle": "2026-06-07T23:05:33.465423Z",
-     "shell.execute_reply": "2026-06-07T23:05:33.464164Z"
+     "iopub.execute_input": "2026-06-07T23:11:57.814188Z",
+     "iopub.status.busy": "2026-06-07T23:11:57.814061Z",
+     "iopub.status.idle": "2026-06-07T23:11:57.912503Z",
+     "shell.execute_reply": "2026-06-07T23:11:57.911927Z"
     }
    },
    "outputs": [
@@ -362,23 +369,23 @@
      "text": [
       "== RESULTADO DO FLEET ASSIGNMENT ==\n",
       "Status: Optimal\n",
-      "Distância Total Minimizada: 28351.05 km\n",
+      "Distância Total Minimizada: 7722.27 km\n",
       "\n",
       "Alocações (Voos Diários):\n",
       "\n",
-      "Trecho: SBMA-SBBR (Voos necessários: 1)\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: SBVH-SBBR (Voos necessários: 1)\n",
+      "Trecho: SBGR-SBBR (Voos necessários: 1)\n",
       " -> ETA6 (Base SBBR): 1 voo(s)\n",
       "\n",
-      "Trecho: SBJI-SBVH (Voos necessários: 1)\n",
-      " -> ETA7 (Base SBMN): 1 voo(s)\n",
-      "\n",
-      "Trecho: SBBE-SBMA (Voos necessários: 1)\n",
-      " -> ETA1 (Base SBBE): 1 voo(s)\n",
-      "\n",
-      "Trecho: SBSJ-SBVT (Voos necessários: 1)\n",
+      "Trecho: SBGL-SBGR (Voos necessários: 1)\n",
       " -> ETA3 (Base SBGL): 1 voo(s)\n"
      ]
     }