first push to hugging face from HaLim

config_page.py

@@ -1102,11 +1102,11 @@ def run_optimization():
         
         # Import and run the optimization (after clearing)
         sys.path.append('src')
-        from models.optimizer_real import solve_fixed_team_weekly
+        from models.optimizer_real import run_optimization_for_week
         
         # Run the optimization
         with st.spinner('Optimizing workforce schedule...'):
-            results = solve_fixed_team_weekly()
+            results = run_optimization_for_week()
         
         if results is None:
             st.error("❌ Optimization failed! The problem may be infeasible with current settings.")
@@ -1118,7 +1118,7 @@ def run_optimization():
         # Store results in session state
         st.session_state.optimization_results = results
         st.success("✅ Optimization completed successfully!")
-        st.experimental_rerun()  # Refresh to show results
+        st.rerun()  # Refresh to show results
         
     except Exception as e:
         st.error(f"❌ Error during optimization: {str(e)}")

data/real_data_excel/converted_csv/{WH_Workforce_Hourly_Pay_Scale_orig.csv → WH_Workforce_Hourly_Pay_Scale.csv}

@@ -1,9 +1,9 @@
-Description,Value (USD),Note
-Humanizer Regular,27.939430832445595,Hourly rate (up to 45 hours / weeks)
-Humanizer  OT,41.90914624866839,Overtime Hourly rate (above 45 hours / week)
-UNICEF - FT GS4 Regular,43.27451923076923,Hourly rate (up to 40 hours / week)
-UNICEF - FT GS4 OT,64.91177884615385,Overtime Hourly rate (above 40 hours / week)
-UNICEF - FT GS5 Regular,44.35817307692308,Hourly rate (up to 40 hours / week)
-UNICEF - FT GS5 OT,66.53725961538461,Overtime Hourly rate (above 40 hours / week)
-UNICEF - FT GS7 Regular,57.666826923076925,Hourly rate (up to 40 hours / week)
-UNICEF - FT GS7 OT,86.50024038461538,Overtime Hourly rate (above 40 hours / week)
+Description,Value (USD),Note,Unnamed: 3,4470.308933191295,28609.97717242429,25,715249.4293106073,4291496.575863644
+Humanizer Regular,27.939430832445595,Hourly rate (up to 45 hours / weeks),,,,,,
+Humanizer  OT,41.90914624866839,Overtime Hourly rate (above 45 hours / week),,,,,,
+UNICEF - FT GS4 Regular,43.27451923076923,Hourly rate (up to 40 hours / week),,,,,,
+UNICEF - FT GS4 OT,64.91177884615385,Overtime Hourly rate (above 40 hours / week),,,,,,
+UNICEF - FT GS5 Regular,44.35817307692308,Hourly rate (up to 40 hours / week),,,,,,
+UNICEF - FT GS5 OT,66.53725961538461,Overtime Hourly rate (above 40 hours / week),,,,,,
+UNICEF - FT GS7 Regular,57.666826923076925,Hourly rate (up to 40 hours / week),,,,,,
+UNICEF - FT GS7 OT,86.50024038461538,Overtime Hourly rate (above 40 hours / week),,,,,,

notebook/data_preprocess.ipynb

@@ -1 +1,16 @@
-
+{
+ "cells": [],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "clean_env_cpd",
+   "language": "python",
+   "name": "clean_env_cpd"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebook/executed_analyze_Realdata.ipynb

@@ -0,0 +1,458 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-02T04:12:14.945798Z",
+     "iopub.status.busy": "2025-07-02T04:12:14.945621Z",
+     "iopub.status.idle": "2025-07-02T04:12:15.606764Z",
+     "shell.execute_reply": "2025-07-02T04:12:15.606337Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Order</th>\n",
+       "      <th>Material Number</th>\n",
+       "      <th>Material description</th>\n",
+       "      <th>Order quantity (GMEIN)</th>\n",
+       "      <th>Basic start date</th>\n",
+       "      <th>Basic finish date</th>\n",
+       "      <th>System Status</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>100033364</td>\n",
+       "      <td>S9992431</td>\n",
+       "      <td>SUB 1/8 NBK, Clinic, Module 1, Medicines</td>\n",
+       "      <td>14</td>\n",
+       "      <td>2025-03-03</td>\n",
+       "      <td>2025-03-07</td>\n",
+       "      <td>REL  PRC  BCRQ MACM SETC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>100034881</td>\n",
+       "      <td>S9991123</td>\n",
+       "      <td>SUB 3/5 f.S9901026 IEHK2017 part 1</td>\n",
+       "      <td>58</td>\n",
+       "      <td>2025-03-17</td>\n",
+       "      <td>2025-03-21</td>\n",
+       "      <td>REL  PRC  BCRQ MACM SETC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>100035124</td>\n",
+       "      <td>S9992442</td>\n",
+       "      <td>SUB 2/3 NBK, Clinic,Module 2,Consumables</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2025-03-14</td>\n",
+       "      <td>2025-03-21</td>\n",
+       "      <td>REL  PRC  BCRQ MACM SETC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>100034003</td>\n",
+       "      <td>S9901042</td>\n",
+       "      <td>IEHK 2024,Basic Equipment UNIT</td>\n",
+       "      <td>800</td>\n",
+       "      <td>2025-03-24</td>\n",
+       "      <td>2025-03-28</td>\n",
+       "      <td>REL  PRC  BCRQ MANC SETC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>100034017</td>\n",
+       "      <td>S9901042</td>\n",
+       "      <td>IEHK 2024,Basic Equipment UNIT</td>\n",
+       "      <td>800</td>\n",
+       "      <td>2025-03-24</td>\n",
+       "      <td>2025-03-28</td>\n",
+       "      <td>REL  PRC  BCRQ MANC SETC</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Order Material Number                      Material description  \\\n",
+       "0  100033364        S9992431  SUB 1/8 NBK, Clinic, Module 1, Medicines   \n",
+       "1  100034881        S9991123        SUB 3/5 f.S9901026 IEHK2017 part 1   \n",
+       "2  100035124        S9992442  SUB 2/3 NBK, Clinic,Module 2,Consumables   \n",
+       "3  100034003        S9901042            IEHK 2024,Basic Equipment UNIT   \n",
+       "4  100034017        S9901042            IEHK 2024,Basic Equipment UNIT   \n",
+       "\n",
+       "   Order quantity (GMEIN) Basic start date Basic finish date  \\\n",
+       "0                      14       2025-03-03        2025-03-07   \n",
+       "1                      58       2025-03-17        2025-03-21   \n",
+       "2                       5       2025-03-14        2025-03-21   \n",
+       "3                     800       2025-03-24        2025-03-28   \n",
+       "4                     800       2025-03-24        2025-03-28   \n",
+       "\n",
+       "              System Status  \n",
+       "0  REL  PRC  BCRQ MACM SETC  \n",
+       "1  REL  PRC  BCRQ MACM SETC  \n",
+       "2  REL  PRC  BCRQ MACM SETC  \n",
+       "3  REL  PRC  BCRQ MANC SETC  \n",
+       "4  REL  PRC  BCRQ MANC SETC  "
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Use kernel 3.7.9 python\n",
+    "#!pip install pandas\n",
+    "import pandas as pd\n",
+    "demand_path = \"data/real_data_excel/converted_csv/COOIS_Released_Prod_Orders.csv\"\n",
+    "df = pd.read_csv(\"../\"+demand_path)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-02T04:12:15.638755Z",
+     "iopub.status.busy": "2025-07-02T04:12:15.638529Z",
+     "iopub.status.idle": "2025-07-02T04:12:15.642455Z",
+     "shell.execute_reply": "2025-07-02T04:12:15.642029Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Order                      int64\n",
+       "Material Number           object\n",
+       "Material description      object\n",
+       "Order quantity (GMEIN)     int64\n",
+       "Basic start date          object\n",
+       "Basic finish date         object\n",
+       "System Status             object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-02T04:12:15.644218Z",
+     "iopub.status.busy": "2025-07-02T04:12:15.644076Z",
+     "iopub.status.idle": "2025-07-02T04:12:15.647938Z",
+     "shell.execute_reply": "2025-07-02T04:12:15.647542Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "현재 날짜 컬럼들의 샘플 데이터:\n",
+      "Basic start date 샘플: 0    2025-03-03\n",
+      "1    2025-03-17\n",
+      "2    2025-03-14\n",
+      "3    2025-03-24\n",
+      "4    2025-03-24\n",
+      "Name: Basic start date, dtype: object\n",
+      "Basic finish date 샘플: 0    2025-03-07\n",
+      "1    2025-03-21\n",
+      "2    2025-03-21\n",
+      "3    2025-03-28\n",
+      "4    2025-03-28\n",
+      "Name: Basic finish date, dtype: object\n",
+      "\n",
+      "현재 데이터 타입:\n",
+      "Basic start date: object\n",
+      "Basic finish date: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check current date columns\n",
+    "print(\"현재 날짜 컬럼들의 샘플 데이터:\")\n",
+    "print(\"Basic start date 샘플:\", df['Basic start date'].head())\n",
+    "print(\"Basic finish date 샘플:\", df['Basic finish date'].head())\n",
+    "print(\"\\n현재 데이터 타입:\")\n",
+    "print(\"Basic start date:\", df['Basic start date'].dtype)\n",
+    "print(\"Basic finish date:\", df['Basic finish date'].dtype)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-02T04:12:15.650086Z",
+     "iopub.status.busy": "2025-07-02T04:12:15.649871Z",
+     "iopub.status.idle": "2025-07-02T04:12:15.661136Z",
+     "shell.execute_reply": "2025-07-02T04:12:15.660788Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔄 날짜 컬럼을 datetime 형태로 변환 중...\n",
+      "✅ 날짜 변환 완료!\n",
+      "\n",
+      "변환 후 데이터 타입:\n",
+      "Basic start date: datetime64[ns]\n",
+      "Basic finish date: datetime64[ns]\n",
+      "\n",
+      "변환 후 샘플 데이터:\n",
+      "  Basic start date Basic finish date\n",
+      "0       2025-03-03        2025-03-07\n",
+      "1       2025-03-17        2025-03-21\n",
+      "2       2025-03-14        2025-03-21\n",
+      "3       2025-03-24        2025-03-28\n",
+      "4       2025-03-24        2025-03-28\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert date columns to datetime\n",
+    "print(\"🔄 날짜 컬럼을 datetime 형태로 변환 중...\")\n",
+    "\n",
+    "# Convert Basic start date and Basic finish date to datetime\n",
+    "df['Basic start date'] = pd.to_datetime(df['Basic start date'], format='%Y-%m-%d', errors='coerce')\n",
+    "df['Basic finish date'] = pd.to_datetime(df['Basic finish date'], format='%Y-%m-%d', errors='coerce')\n",
+    "\n",
+    "print(\"✅ 날짜 변환 완료!\")\n",
+    "print(\"\\n변환 후 데이터 타입:\")\n",
+    "print(\"Basic start date:\", df['Basic start date'].dtype)\n",
+    "print(\"Basic finish date:\", df['Basic finish date'].dtype)\n",
+    "\n",
+    "print(\"\\n변환 후 샘플 데이터:\")\n",
+    "print(df[['Basic start date', 'Basic finish date']].head())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-02T04:12:15.662982Z",
+     "iopub.status.busy": "2025-07-02T04:12:15.662804Z",
+     "iopub.status.idle": "2025-07-02T04:12:15.669930Z",
+     "shell.execute_reply": "2025-07-02T04:12:15.669520Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📁 Found 0 CSV files to process...\n",
+      "============================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import glob\n",
+    "\n",
+    "def convert_dates_in_csv_files(csv_directory):\n",
+    "    \"\"\"\n",
+    "    Convert date columns in all CSV files to datetime format\n",
+    "    \n",
+    "    Args:\n",
+    "        csv_directory (str): Directory containing CSV files\n",
+    "    \"\"\"\n",
+    "    # Find all CSV files\n",
+    "    csv_files = glob.glob(os.path.join(csv_directory, \"*.csv\"))\n",
+    "    \n",
+    "    # Date column patterns to look for\n",
+    "    date_columns = [\n",
+    "        'Basic start date', 'Basic finish date', \n",
+    "        'PO Delivery Date', 'Valid From', 'Valid To',\n",
+    "        'Creation Date of Material', 'Pack date'\n",
+    "    ]\n",
+    "    \n",
+    "    print(f\"📁 Found {len(csv_files)} CSV files to process...\")\n",
+    "    print(\"=\" * 60)\n",
+    "    \n",
+    "    results = {}\n",
+    "    \n",
+    "    for csv_file in csv_files:\n",
+    "        filename = os.path.basename(csv_file)\n",
+    "        print(f\"\\n📄 Processing: {filename}\")\n",
+    "        \n",
+    "        try:\n",
+    "            # Read CSV file\n",
+    "            df = pd.read_csv(csv_file)\n",
+    "            converted_columns = []\n",
+    "            \n",
+    "            # Check each potential date column\n",
+    "            for date_col in date_columns:\n",
+    "                if date_col in df.columns:\n",
+    "                    # Check if it's currently object type (string)\n",
+    "                    if df[date_col].dtype == 'object':\n",
+    "                        # Try to convert to datetime\n",
+    "                        original_samples = df[date_col].dropna().head(3).tolist()\n",
+    "                        df[date_col] = pd.to_datetime(df[date_col], errors='coerce')\n",
+    "                        converted_columns.append(date_col)\n",
+    "                        print(f\"   ✅ Converted '{date_col}': {original_samples}\")\n",
+    "            \n",
+    "            if converted_columns:\n",
+    "                # Save the updated CSV\n",
+    "                df.to_csv(csv_file, index=False)\n",
+    "                results[filename] = converted_columns\n",
+    "                print(f\"   💾 Saved updated file with {len(converted_columns)} converted columns\")\n",
+    "            else:\n",
+    "                print(f\"   ℹ️  No date columns found to convert\")\n",
+    "                results[filename] = []\n",
+    "                \n",
+    "        except Exception as e:\n",
+    "            print(f\"   ❌ Error processing {filename}: {e}\")\n",
+    "            results[filename] = f\"Error: {e}\"\n",
+    "    \n",
+    "    return results\n",
+    "\n",
+    "# Convert dates in all CSV files\n",
+    "csv_dir = \"../data/converted_csv\"\n",
+    "conversion_results = convert_dates_in_csv_files(csv_dir)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-02T04:12:15.671770Z",
+     "iopub.status.busy": "2025-07-02T04:12:15.671585Z",
+     "iopub.status.idle": "2025-07-02T04:12:15.678103Z",
+     "shell.execute_reply": "2025-07-02T04:12:15.677635Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "============================================================\n",
+      "📊 DATE CONVERSION SUMMARY\n",
+      "============================================================\n",
+      "\n",
+      "🎯 RESULTS:\n",
+      "   - Total files processed: 0\n",
+      "   - Files with date conversions: 0\n",
+      "   - Total date columns converted: 0\n",
+      "\n",
+      "🔍 VERIFICATION - Current dataframe:\n",
+      "   - Basic start date type: datetime64[ns]\n",
+      "   - Basic finish date type: datetime64[ns]\n",
+      "\n",
+      "   Sample converted dates:\n",
+      "       Order Basic start date Basic finish date\n",
+      "0  100033364       2025-03-03        2025-03-07\n",
+      "1  100034881       2025-03-17        2025-03-21\n",
+      "2  100035124       2025-03-14        2025-03-21\n",
+      "3  100034003       2025-03-24        2025-03-28\n",
+      "4  100034017       2025-03-24        2025-03-28\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Summary of conversion results\n",
+    "print(\"\\n\" + \"=\" * 60)\n",
+    "print(\"📊 DATE CONVERSION SUMMARY\")\n",
+    "print(\"=\" * 60)\n",
+    "\n",
+    "total_files = len(conversion_results)\n",
+    "files_with_conversions = 0\n",
+    "total_columns_converted = 0\n",
+    "\n",
+    "for filename, columns in conversion_results.items():\n",
+    "    if isinstance(columns, list) and len(columns) > 0:\n",
+    "        files_with_conversions += 1\n",
+    "        total_columns_converted += len(columns)\n",
+    "        print(f\"✅ {filename}: {len(columns)} columns converted\")\n",
+    "        for col in columns:\n",
+    "            print(f\"    - {col}\")\n",
+    "    elif isinstance(columns, list):\n",
+    "        print(f\"ℹ️  {filename}: No date columns found\")\n",
+    "    else:\n",
+    "        print(f\"❌ {filename}: {columns}\")\n",
+    "\n",
+    "print(f\"\\n🎯 RESULTS:\")\n",
+    "print(f\"   - Total files processed: {total_files}\")\n",
+    "print(f\"   - Files with date conversions: {files_with_conversions}\")\n",
+    "print(f\"   - Total date columns converted: {total_columns_converted}\")\n",
+    "\n",
+    "# Test the conversion on our main dataframe\n",
+    "print(f\"\\n🔍 VERIFICATION - Current dataframe:\")\n",
+    "print(f\"   - Basic start date type: {df['Basic start date'].dtype}\")\n",
+    "print(f\"   - Basic finish date type: {df['Basic finish date'].dtype}\")\n",
+    "print(f\"\\n   Sample converted dates:\")\n",
+    "print(df[['Order', 'Basic start date', 'Basic finish date']].head())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebook/version1.ipynb

@@ -0,0 +1,108 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mathematical Optimization Model for Labor Scheduling\n",
+    "\n",
+    "##  Indices and Sets\n",
+    "- $E$: Set of employee types (e.g., permanent, contract, temporary)\n",
+    "- $S$: Set of shift types (e.g., usual_time, evening_shift, overtime)\n",
+    "\n",
+    "*We have a target date. The date is a specific order deadline and on oneday we proceed the whole task of one day, and goes no further than 1 day order amount.\n",
+    "\n",
+    "## Parameters\n",
+    "- $h_s$: Hours per shift type $s$\n",
+    "- $m_e$: Productivity per hour for employee type $e$ \n",
+    "    + Some employee type / work type have more/less than 1 hour productivity unit)\n",
+    "- $c_{e,s}$: Cost per hour for employee type $e$ in shift type $s$\n",
+    "- $L$: Total unit productivity required\n",
+    "- $A_e$: Number of available employees of type $e$\n",
+    "\n",
+    "## Decision Variables\n",
+    "- $x_{e,s} \\in \\mathbb{Z}_{\\geq 0}$: Number of employees of type $e$ assigned to shift type $s$\n",
+    "    + $e$ : regular, temporary.. etc. \n",
+    "    + $s$ : work hour type (evening shift, regular, late hour). \n",
+    "    + Based on the employee type and work hour type, the productivity per hour and cost per hour differs\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Objective Function\n",
+    "\n",
+    "Minimize total labor cost:\n",
+    "\n",
+    "$$\n",
+    "\\min \\sum_{e \\in E} \\sum_{s \\in S} x_{e,s} \\cdot h_s \\cdot c_{e,s}\n",
+    "$$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Constraints\n",
+    "\n",
+    "1. **Labor Demand Satisfaction**\n",
+    "\n",
+    "Ensure the total unit productivity covers the required total productivity:\n",
+    "\n",
+    "$$\n",
+    "\\sum_{e \\in E} \\sum_{s \\in S} x_{e,s} \\cdot h_s \\cdot m_e \\geq L\n",
+    "$$\n",
+    "\n",
+    "2. **Employee Availability**\n",
+    "\n",
+    "- Usual and evening shift is a discrete work type\n",
+    "    + Those who come in the usual cannot work during the evening shift\n",
+    "\n",
+    "$$\n",
+    "x_{e,\\text{usual}} + x_{e,\\text{evening}} \\leq A_e \\quad \\forall e \\in E\n",
+    "$$\n",
+    "\n",
+    "3. **Overtime Limitation**\n",
+    "\n",
+    "Overtime must not exceed regular shift assignments. \n",
+    "\n",
+    "(Because one who works overtime must work during the regular shift)\n",
+    "\n",
+    "$$\n",
+    "x_{e,\\text{overtime}} \\leq x_{e,\\text{usual}} \\quad \\forall e \\in E\n",
+    "$$\n",
+    "\n",
+    "4. **Non-negativity and Integrality**\n",
+    "\n",
+    "All decision variables must be non-negative integers:\n",
+    "\n",
+    "$$\n",
+    "x_{e,s} \\in \\mathbb{Z}_{\\geq 0} \\quad \\forall e \\in E, s \\in S\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements-viz.txt

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+# Optional visualization dependencies for enhanced hierarchy dashboard
+# Install with: pip install -r requirements-viz.txt
+
+networkx>=2.8.0       # For dependency network graphs
+plotly>=5.0.0         # For interactive charts (should already be installed)
+pandas>=1.3.0         # For data processing (should already be installed)
+numpy>=1.20.0         # For numerical operations (should already be installed)