Cleaning minor lines and unused variables

notebook/analyze_Realdata.ipynb

@@ -774,14 +774,11 @@
     "\n",
     "plt.figure(figsize=(10, 6))\n",
     "\n",
-    "# 날짜 기준으로 집계\n",
     "grouped = df_merged.groupby('Basic finish date').sum()\n",
     "\n",
-    "# 선 그래프 그리기\n",
     "plt.plot(grouped.index, grouped['required_humanizer'], label='Humanizer')\n",
     "plt.plot(grouped.index, grouped['required_unicef'], label='UNICEF')\n",
     "\n",
-    "# 축 이름, 범례, 제목 등\n",
     "plt.xlabel('Basic finish date')\n",
     "plt.ylabel('Required labor')\n",
     "plt.title('Labor Requirement Over Time')\n",

notebook/executed_analyze_Realdata.ipynb

@@ -1,458 +0,0 @@
-{
- "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
-}

src/models/optimizer_real.py

@@ -11,7 +11,7 @@ from ortools.linear_solver import pywraplp
 from math import ceil
 from src.config.constants import ShiftType, LineType, KitLevel
 
-# ---- config import (프로젝트 경로에 맞춰 조정) ----
+# ---- config import ----
 from src.config.optimization_config import (
     get_date_span,            # DYNAMIC: Get date span dynamically
     get_product_list,         # DYNAMIC: list of products (e.g., ['A','B',...])
@@ -25,7 +25,6 @@ from src.config.optimization_config import (
     MAX_HOUR_PER_PERSON_PER_DAY,     # e.g., 14
     get_max_hour_per_shift_per_person,   # DYNAMIC: {1: hours, 2: hours, 3: hours}
     get_max_parallel_workers,        # DYNAMIC: {6: max_workers, 7: max_workers}
-    FIXED_STAFF_CONSTRAINT_MODE,     # not used in fixed-team model (동시 투입이라 무의미)
     get_team_requirements,    # DYNAMIC: {emp_type: {product: team_size}} from Kits_Calculation.csv
     get_payment_mode_config,         # DYNAMIC: {shift: 'bulk'/'partial'} payment mode configuration
     get_kit_line_match,              # DYNAMIC: Get kit line match lazily