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MMC-C/problem1-1终稿.ipynb

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完美了
2024-09-08 08:15:40 +00:00
{
"cells": [
{
"cell_type": "code",
"id": "e19ec4ae5347c678",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:17.258978Z",
"start_time": "2024-09-08T08:14:16.937551Z"
}
},
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"pd.options.display.max_columns = 100"
],
"outputs": [],
"execution_count": 1
},
{
"cell_type": "code",
"id": "initial_id",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:17.666091Z",
"start_time": "2024-09-08T08:14:17.259867Z"
}
},
"source": [
"\n",
"df_crop_details = pd.read_excel('./data/2.xlsx', sheet_name=1)"
],
"outputs": [],
"execution_count": 2
},
{
"cell_type": "code",
"id": "1d2a51b3414be94",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:17.681548Z",
"start_time": "2024-09-08T08:14:17.668092Z"
}
},
"source": [
"# 去除空格\n",
"df_crop_details['cropName'] = df_crop_details['cropName'].apply(lambda x: x.strip())\n"
],
"outputs": [],
"execution_count": 3
},
{
"cell_type": "code",
"id": "514cd9136d9ca341",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:17.712692Z",
"start_time": "2024-09-08T08:14:17.682481Z"
}
},
"source": [
"df_crop_planting = pd.read_excel('./data/2.xlsx', sheet_name=0)\n",
"# 照例去除一下空格\n",
"df_crop_planting['cropName'] = df_crop_planting['cropName'].apply(lambda x: x.strip())\n",
"# ffill\n",
"df_crop_planting['landName'] = df_crop_planting['landName'].ffill()"
],
"outputs": [],
"execution_count": 4
},
{
"cell_type": "code",
"id": "1503f8b642c842db",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:17.743562Z",
"start_time": "2024-09-08T08:14:17.714837Z"
}
},
"source": [
"df_land = pd.read_excel('./data/1.xlsx', sheet_name=0)\n",
"# 去除landType和landName的空格\n",
"df_land['landType'] = df_land['landType'].apply(lambda x: x.strip())\n",
"df_land['landName'] = df_land['landName'].apply(lambda x: x.strip())"
],
"outputs": [],
"execution_count": 5
},
{
"cell_type": "code",
"id": "3cdf51a9a9d4d30f",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:17.759420Z",
"start_time": "2024-09-08T08:14:17.744880Z"
}
},
"source": [
"# 计算利润\n",
"unit_profit_lsc = []\n",
"for line in df_crop_details.values:\n",
" s = str(line[7]).split('-')\n",
" unit_profit_lsc.append((float(s[0]) + float(s[1])) / 2 * line[5] - line[6])\n",
"df_crop_details['unitProfit'] = unit_profit_lsc"
],
"outputs": [],
"execution_count": 6
},
{
"cell_type": "code",
"id": "569016a9b90f841b",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:17.790551Z",
"start_time": "2024-09-08T08:14:17.760229Z"
}
},
"source": [
"df_crop_type_land = pd.read_excel('./data/1.xlsx', sheet_name=1)\n",
"# 老规矩，去掉cropName和cropType的空格\n",
"df_crop_type_land['cropType'] = df_crop_type_land['cropType'].apply(lambda x: x.strip())\n",
"df_crop_type_land['cropName'] = df_crop_type_land['cropName'].apply(lambda x: x.strip())"
],
"outputs": [],
"execution_count": 7
},
{
"cell_type": "code",
"id": "a7661d84217b578c",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:17.806633Z",
"start_time": "2024-09-08T08:14:17.791553Z"
}
},
"source": [
"# 搞一下季节和年份的集合\n",
"SeasonType = [\"单季\", \"第一季\", \"第二季\"]\n",
"SeasonDict = {\"单季\": 1, \"第一季\": 1, \"第二季\": 2}\n",
"SeasonNum = [1, 2]\n",
"years = [2024, 2025, 2026, 2027, 2028, 2029, 2030]"
],
"outputs": [],
"execution_count": 8
},
{
"cell_type": "code",
"id": "4a4e06a1f2d4d8ab",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:17.822646Z",
"start_time": "2024-09-08T08:14:17.807607Z"
}
},
"source": [
"# 枚举地块类型\n",
"LandType = {\"A\": \"平旱地\", \"B\": \"梯田\", \"C\": \"山坡地\", \"D\": \"水浇地\", \"E\": \"普通大棚\", \"F\": \"智慧大棚\"}"
],
"outputs": [],
"execution_count": 9
},
{
"cell_type": "code",
"id": "cc938565a63a8129",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:17.838210Z",
"start_time": "2024-09-08T08:14:17.823659Z"
}
},
"source": [
"# 枚举地块\n",
"LandName = df_crop_planting['landName'].unique()"
],
"outputs": [],
"execution_count": 10
},
{
"cell_type": "code",
"id": "dae53a6215cea525",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:17.853788Z",
"start_time": "2024-09-08T08:14:17.839182Z"
}
},
"source": [
"# 枚举地块面积，取df_land的landName为key，landArea为value\n",
"LandArea = {x: df_land[df_land['landName'] == x]['landArea'].values[0] for x in LandName}"
],
"outputs": [],
"execution_count": 11
},
{
"cell_type": "code",
"id": "7a0e6cc209d93b56",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:17.869293Z",
"start_time": "2024-09-08T08:14:17.854772Z"
}
},
"source": [
"# 读入作物名称\n",
"CropName = df_crop_details['cropName'].unique()"
],
"outputs": [],
"execution_count": 12
},
{
"cell_type": "code",
"id": "8a280f918bb3139a",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:17.900877Z",
"start_time": "2024-09-08T08:14:17.870289Z"
}
},
"source": [
"# 作物分类\n",
"CropType = {x: df_crop_type_land[df_crop_type_land['cropType'] == x]['cropName'].values for x in\n",
" df_crop_type_land['cropType'].values}\n",
"CropType['粮食（除了水稻）'] = CropType['粮食'][:-1] # 这样不太好，但能用\n",
"CropType['豆类'] = np.array(list(CropType['粮食（豆类）']) + list(CropType['蔬菜（豆类）']), dtype=object)\n",
"CropType['全粮食'] = np.array(list(CropType['粮食']) + list(CropType['粮食（豆类）']), dtype=object)\n",
"CropType['全粮食（除了水稻）'] = np.array(list(CropType['粮食'][:-1]) + list(CropType['粮食（豆类）']), dtype=object)\n",
"CropType['全蔬菜'] = np.array(list(CropType['蔬菜']) + list(CropType['蔬菜（豆类）']), dtype=object)\n",
"CropType['特殊蔬菜'] = np.array([\"大白菜\", \"白萝卜\", \"红萝卜\"], dtype=object)\n",
"CropType[\"普通蔬菜\"] = np.array([c for c in CropType['全蔬菜'] if c not in CropType['特殊蔬菜']], dtype=object)\n",
"CropType"
],
"outputs": [
{
"data": {
"text/plain": [
"{'粮食（豆类）': array(['黄豆', '黑豆', '红豆', '绿豆', '爬豆'], dtype=object),\n",
" '粮食': array(['小麦', '玉米', '谷子', '高粱', '黍子', '荞麦', '南瓜', '红薯', '莜麦', '大麦', '水稻'],\n",
" dtype=object),\n",
" '蔬菜（豆类）': array(['豇豆', '刀豆', '芸豆'], dtype=object),\n",
" '蔬菜': array(['土豆', '西红柿', '茄子', '菠菜', '青椒', '菜花', '包菜', '油麦菜', '小青菜', '黄瓜',\n",
" '生菜', '辣椒', '空心菜', '黄心菜', '芹菜', '大白菜', '白萝卜', '红萝卜'], dtype=object),\n",
" '食用菌': array(['榆黄菇', '香菇', '白灵菇', '羊肚菌'], dtype=object),\n",
" '粮食（除了水稻）': array(['小麦', '玉米', '谷子', '高粱', '黍子', '荞麦', '南瓜', '红薯', '莜麦', '大麦'],\n",
" dtype=object),\n",
" '豆类': array(['黄豆', '黑豆', '红豆', '绿豆', '爬豆', '豇豆', '刀豆', '芸豆'], dtype=object),\n",
" '全粮食': array(['小麦', '玉米', '谷子', '高粱', '黍子', '荞麦', '南瓜', '红薯', '莜麦', '大麦', '水稻',\n",
" '黄豆', '黑豆', '红豆', '绿豆', '爬豆'], dtype=object),\n",
" '全粮食（除了水稻）': array(['小麦', '玉米', '谷子', '高粱', '黍子', '荞麦', '南瓜', '红薯', '莜麦', '大麦', '黄豆',\n",
" '黑豆', '红豆', '绿豆', '爬豆'], dtype=object),\n",
" '全蔬菜': array(['土豆', '西红柿', '茄子', '菠菜', '青椒', '菜花', '包菜', '油麦菜', '小青菜', '黄瓜',\n",
" '生菜', '辣椒', '空心菜', '黄心菜', '芹菜', '大白菜', '白萝卜', '红萝卜', '豇豆', '刀豆',\n",
" '芸豆'], dtype=object),\n",
" '特殊蔬菜': array(['大白菜', '白萝卜', '红萝卜'], dtype=object),\n",
" '普通蔬菜': array(['土豆', '西红柿', '茄子', '菠菜', '青椒', '菜花', '包菜', '油麦菜', '小青菜', '黄瓜',\n",
" '生菜', '辣椒', '空心菜', '黄心菜', '芹菜', '豇豆', '刀豆', '芸豆'], dtype=object)}"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 13
},
{
"cell_type": "code",
"id": "22f9730e3d9c1016",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:18.089134Z",
"start_time": "2024-09-08T08:14:17.903431Z"
}
},
"source": [
"# 亩产量\n",
"unit_yield_lsc = {\n",
" l: {\n",
" 1: {\n",
" c: df_crop_details[\n",
" (df_crop_details['cropName'] == c) &\n",
" (df_crop_details['cropLandType'] == l) &\n",
" ((df_crop_details['season'] == \"单季\") |\n",
" (df_crop_details['season'] == \"第一季\"))\n",
" ]['unitYield'].values[0] if df_crop_details[\n",
" (df_crop_details['cropName'] == c) &\n",
" (df_crop_details['cropLandType'] == l) &\n",
" ((df_crop_details['season'] == \"单季\") |\n",
" (df_crop_details['season'] == \"第一季\"))\n",
" ]['unitYield'].values.size > 0 else 0\n",
" for c in CropName\n",
" },\n",
" 2: {\n",
" c: df_crop_details[\n",
" (df_crop_details['cropName'] == c) &\n",
" (df_crop_details['cropLandType'] == l) &\n",
" (df_crop_details['season'] == \"第二季\")\n",
" ]['unitYield'].values[0] if df_crop_details[\n",
" (df_crop_details['cropName'] == c) &\n",
" (df_crop_details['cropLandType'] == l) &\n",
" (df_crop_details['season'] == \"第二季\")][\n",
" 'unitYield'].values.size > 0 else 0\n",
" for c in CropName\n",
" }\n",
" }\n",
" for l in LandType.values()\n",
"}\n"
],
"outputs": [],
"execution_count": 14
},
{
"cell_type": "code",
"id": "99dbe4ca6c54db0b",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:18.292866Z",
"start_time": "2024-09-08T08:14:18.089960Z"
}
},
"source": [
"# 亩利润\n",
"unit_profit_lsc = {\n",
" l: {\n",
" 1: {\n",
" c: df_crop_details[\n",
" (df_crop_details['cropName'] == c) &\n",
" (df_crop_details['cropLandType'] == l) &\n",
" ((df_crop_details['season'] == \"单季\") |\n",
" (df_crop_details['season'] == \"第一季\"))\n",
" ]['unitProfit'].values[0] if df_crop_details[\n",
" (df_crop_details['cropName'] == c) &\n",
" (df_crop_details['cropLandType'] == l) &\n",
" ((df_crop_details['season'] == \"单季\") |\n",
" (df_crop_details['season'] == \"第一季\"))\n",
" ]['unitProfit'].values.size > 0 else 0\n",
" for c in CropName\n",
" },\n",
" 2: {\n",
" c: df_crop_details[\n",
" (df_crop_details['cropName'] == c) &\n",
" (df_crop_details['cropLandType'] == l) &\n",
" (df_crop_details['season'] == \"第二季\")\n",
" ]['unitProfit'].values[0] if df_crop_details[\n",
" (df_crop_details['cropName'] == c) &\n",
" (df_crop_details['cropLandType'] == l) &\n",
" (df_crop_details['season'] == \"第二季\")\n",
" ]['unitProfit'].values.size > 0 else 0\n",
" for c in CropName\n",
" }\n",
" }\n",
" for l in LandType.values()\n",
"}\n"
],
"outputs": [],
"execution_count": 15
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:18.462863Z",
"start_time": "2024-09-08T08:14:18.293865Z"
}
},
"cell_type": "code",
"source": [
"# 亩成本\n",
"unit_cost_lsc = {\n",
" l: {\n",
" 1: {\n",
" c: df_crop_details[\n",
" (df_crop_details['cropName'] == c) &\n",
" (df_crop_details['cropLandType'] == l) &\n",
" ((df_crop_details['season'] == \"单季\") |\n",
" (df_crop_details['season'] == \"第一季\"))\n",
" ]['cost'].values[0] if df_crop_details[\n",
" (df_crop_details['cropName'] == c) &\n",
" (df_crop_details['cropLandType'] == l) &\n",
" ((df_crop_details['season'] == \"单季\") |\n",
" (df_crop_details['season'] == \"第一季\"))\n",
" ]['cost'].values.size > 0 else 0\n",
" for c in CropName\n",
" },\n",
" 2: {\n",
" c: df_crop_details[\n",
" (df_crop_details['cropName'] == c) &\n",
" (df_crop_details['cropLandType'] == l) &\n",
" (df_crop_details['season'] == \"第二季\")\n",
" ]['cost'].values[0] if df_crop_details[\n",
" (df_crop_details['cropName'] == c) &\n",
" (df_crop_details['cropLandType'] == l) &\n",
" (df_crop_details['season'] == \"第二季\")\n",
" ]['cost'].values.size > 0 else 0\n",
" for c in CropName\n",
" }\n",
" }\n",
" for l in LandType.values()\n",
"}\n"
],
"id": "1c8ad73eaea8be7a",
"outputs": [],
"execution_count": 16
},
{
"cell_type": "code",
"id": "13f2ccc7aa215d4c",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:18.478343Z",
"start_time": "2024-09-08T08:14:18.463669Z"
}
},
"source": [
"# 每种作物的总需求\n",
"crop_demand = {\n",
" c: 0\n",
" for c in CropName\n",
"}\n",
"\n",
"# 这里需要另一张表\n",
"# 代码独立出来移到上面去了\n",
"\n",
"for line in df_crop_planting.values:\n",
" # 面积*该土地类型的亩产量 面积 地块类型字典 地块类型 季节 作物名称\n",
" crop_demand[line[2]] += line[4] * unit_yield_lsc[LandType[line[0][0]]][SeasonDict[line[5]]][line[2]]\n"
],
"outputs": [],
"execution_count": 17
},
{
"cell_type": "code",
"id": "5d09872708d40da4",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:18.525244Z",
"start_time": "2024-09-08T08:14:18.480029Z"
}
},
"source": [
"# 准备开搞\n",
"from pulp import LpMaximize, LpProblem, LpVariable, lpSum, value, PULP_CBC_CMD, LpContinuous, LpBinary\n",
"import pulp"
],
"outputs": [],
"execution_count": 18
},
{
"cell_type": "code",
"id": "b6eff87c7a762769",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:18.541546Z",
"start_time": "2024-09-08T08:14:18.527235Z"
}
},
"source": [
"model = LpProblem(\"Crop_Planting_Optimization_with_Specific_Rules\", LpMaximize)"
],
"outputs": [],
"execution_count": 19
},
{
"cell_type": "code",
"id": "4cb129e20385a7ee",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:18.587814Z",
"start_time": "2024-09-08T08:14:18.542556Z"
}
},
"source": [
"\n",
"t = []\n",
"for y in years:\n",
" for l in LandName:\n",
" for s in SeasonNum:\n",
" if l[0] in [\"A\", \"B\", \"C\"] and s == 1:\n",
" for c in CropType[\"全粮食（除了水稻）\"]:\n",
" t.append((c, l, y, s))\n",
" if l[0] == \"D\":\n",
" if s == 1:\n",
" for c in list(CropType[\"普通蔬菜\"]) + [\"水稻\"]:\n",
" t.append((c, l, y, s))\n",
" if s == 2:\n",
" for c in CropType[\"特殊蔬菜\"]:\n",
" t.append((c, l, y, s))\n",
" if l[0] == \"E\":\n",
" if s == 1:\n",
" for c in list(CropType[\"普通蔬菜\"]):\n",
" t.append((c, l, y, s))\n",
" if s == 2:\n",
" for c in CropType[\"食用菌\"]:\n",
" t.append((c, l, y, s))\n",
" if l[0] == \"F\":\n",
" for s in SeasonNum:\n",
" for c in CropType[\"普通蔬菜\"]:\n",
" t.append((c, l, y, s))\n",
"\n",
"\n",
"# X"
],
"outputs": [],
"execution_count": 20
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:18.618675Z",
"start_time": "2024-09-08T08:14:18.589714Z"
}
},
"cell_type": "code",
"source": [
"# 地块l在y年的s季的种植c的量\n",
"X = LpVariable.dicts(\"X\", t,\n",
" lowBound=0,\n",
" cat=LpContinuous\n",
" )"
],
"id": "ca23e006a68f2993",
"outputs": [],
"execution_count": 21
},
{
"cell_type": "code",
"id": "9dab983ca7b3b607",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:18.649531Z",
"start_time": "2024-09-08T08:14:18.619566Z"
}
},
"source": [
"# 地块l在y年的s季是否种植了c\n",
"Y = LpVariable.dicts(\"Y\", t, cat=LpBinary)"
],
"outputs": [],
"execution_count": 22
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:18.664701Z",
"start_time": "2024-09-08T08:14:18.650399Z"
}
},
"cell_type": "code",
"source": [
"tt = set(t)\n",
"\n",
"\n",
"def is_feasible(c, l, y, s):\n",
" return (c, l, y, s) in tt"
],
"id": "bf16c7f3ea10152f",
"outputs": [],
"execution_count": 23
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:19.068077Z",
"start_time": "2024-09-08T08:14:18.665729Z"
}
},
"cell_type": "code",
"source": [
"# 确保与X进行01约束\n",
"for c in CropName:\n",
" for l in LandName:\n",
" for y in years:\n",
" for s in SeasonNum:\n",
" # if 1:\n",
" if is_feasible(c, l, y, s):\n",
" # 如果种植了作物，则种植面积大于0\n",
" model += X[c, l, y, s] <= 100 * Y[c, l, y, s], f\"PlantingConstraint1_{c}_{l}_{y}_{s}\"\n",
" # 如果未种植作物，则种植面积为0\n",
" model += X[c, l, y, s] >= 0.001 * Y[c, l, y, s], f\"PlantingConstraint2_{c}_{l}_{y}_{s}\"\n",
"\n",
"min_area = 0.2\n",
"for c in CropName:\n",
" for l in LandName:\n",
" for y in years:\n",
" for s in SeasonNum:\n",
真完美了
2024-09-08 08:17:07 +00:00
" if is_feasible(c, l, y, s): # 最小面积约束和加强版01约束\n",
完美了
2024-09-08 08:15:40 +00:00
" model += X[c, l, y, s] <= LandArea[l] * Y[c, l, y, s], f\"LandAreaConstraint_{c}_{l}_{y}_{s}\"\n",
" model += X[c, l, y, s] >= min_area * (\n",
" 1 if Y[c, l, y, s] else 0), f\"MinAreaConstraint_{c}_{l}_{y}_{s}\""
],
"id": "603175d36624172f",
"outputs": [],
"execution_count": 24
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:19.177480Z",
"start_time": "2024-09-08T08:14:19.069076Z"
}
},
"cell_type": "code",
"source": [
"# 初始化总目标函数的表达式\n",
"total_profit = lpSum([]) # 空的lpSum用于初始化\n",
"\n",
"# 累积每个作物、每个年份的利润和惩罚\n",
"for c in CropName:\n",
" for y in years:\n",
" # 计算利润部分\n",
" profit_term = lpSum(\n",
" X[c, l, y, s] * unit_profit_lsc[LandType[l[0]]][s][c]\n",
" for l in LandName for s in SeasonNum if is_feasible(c, l, y, s)\n",
" )\n",
真完美了
2024-09-08 08:17:07 +00:00
"\n",
完美了
2024-09-08 08:15:40 +00:00
" # 惩罚超过需求的部分：将每个地块、每个季节的单位成本与种植量相乘\n",
" penalty_term = lpSum(\n",
" X[c, l, y, s] * unit_cost_lsc[LandType[l[0]]][s][c]\n",
" for l in LandName for s in SeasonNum if is_feasible(c, l, y, s)\n",
" ) - crop_demand[c] * lpSum(\n",
" unit_cost_lsc[LandType[l[0]]][s][c]\n",
" for l in LandName for s in SeasonNum if is_feasible(c, l, y, s)\n",
" )\n",
真完美了
2024-09-08 08:17:07 +00:00
"\n",
完美了
2024-09-08 08:15:40 +00:00
" # 累积到总目标函数中\n",
" total_profit += profit_term - penalty_term\n",
"\n",
"# 一次性将目标函数添加到模型中\n",
"model += total_profit, \"total_profit\"\n"
],
"id": "4505b0f2fe6bf93d",
"outputs": [],
"execution_count": 25
},
{
"cell_type": "code",
"id": "7e13de7700d38f19",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:19.225057Z",
"start_time": "2024-09-08T08:14:19.195511Z"
}
},
"source": [
"# 条件2： 每年每季每块地的种植面积不能超过该地块的总面积\n",
"for l in LandName:\n",
" for y in years:\n",
" for s in SeasonNum:\n",
" model += lpSum(X[c, l, y, s] for c in CropName if is_feasible(c, l, y, s)) <= LandArea[\n",
" l], f\"LandAreaConstraint_{l}_{y}_{s}\""
],
"outputs": [],
"execution_count": 27
},
{
"cell_type": "code",
"id": "1f6ac4323825f28b",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:19.240711Z",
"start_time": "2024-09-08T08:14:19.226182Z"
}
},
"source": [
"# 条件3： 平旱地、梯田和山坡地每年适宜单季种植[粮食类作物（水稻除外）]。因为除了[粮食类作物（水稻除外）]以外的作物似乎已经被上面给约束了，所以大概不需要做限制了吧\n",
"for l in LandName:\n",
" if l[0] in [\"A\", \"B\", \"C\"]: # [\"平旱地\", \"梯田\", \"山坡地\"]\n",
" for y in years:\n",
" model += lpSum(\n",
" Y[c, l, y, 2] for c in CropName if is_feasible(c, l, y, 2)) == 0, f\"SingleSeasonConstraint1_{y}_{l}\"\n",
"\n"
],
"outputs": [],
"execution_count": 28
},
{
"cell_type": "code",
"id": "c0e9b6b37092f0e7",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:19.256292Z",
"start_time": "2024-09-08T08:14:19.241709Z"
}
},
"source": [
"# 条件4： 水浇地每年可以单季种植水稻或两季种植蔬菜作物。\n",
"for l in LandName:\n",
" if l[0] == \"D\":\n",
" for y in years:\n",
" for c in CropType[\"全蔬菜\"]:\n",
真完美了
2024-09-08 08:17:07 +00:00
" if is_feasible(c, l, y, 2):\n",
完美了
2024-09-08 08:15:40 +00:00
" model += Y[\"水稻\", l, y, 1] + Y[c, l, y, 2] <= 1, f\"IrrigatedConstraint0_{l}_{y}_{c}\"\n"
],
"outputs": [],
"execution_count": 29
},
{
"cell_type": "code",
"id": "e8fb2dea1326a05b",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:19.287009Z",
"start_time": "2024-09-08T08:14:19.257188Z"
}
},
"source": [
"# 条件10： 别种得太碎了，啥意思？ 每种作物不超过4块地吧。\n",
"for c in CropName:\n",
" for y in years:\n",
" for s in SeasonNum:\n",
" model += lpSum(\n",
" Y[c, l, y, s] for l in LandName if is_feasible(c, l, y, s)) <= 3, f\"MaxCropTypeConstraint_{c}_{y}_{s}\""
],
"outputs": [],
"execution_count": 30
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:19.333885Z",
"start_time": "2024-09-08T08:14:19.288072Z"
}
},
"cell_type": "code",
"source": [
"# 条件11： 每块地最大种植作物种类数3\n",
"for l in LandName:\n",
" for y in years:\n",
" for s in SeasonNum:\n",
" model += lpSum(\n",
" Y[c, l, y, s] for c in CropName if is_feasible(c, l, y, s)) <= 3, f\"MaxLandConstraint_{l}_{y}_{s}\""
],
"id": "b8d9ae82090818ec",
"outputs": [],
"execution_count": 31
},
{
"cell_type": "code",
"id": "49f1490823c6af3c",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:19.411639Z",
"start_time": "2024-09-08T08:14:19.335783Z"
}
},
"source": [
"# 条件12： 最小种植面积：0.2吧\n",
"for l in LandName:\n",
" for y in years:\n",
" for s in SeasonNum:\n",
" for c in CropName:\n",
" if is_feasible(c, l, y, s):\n",
" model += X[c, l, y, s] >= 0.2 * (\n",
" 1 if X[c, l, y, s] else 0), f\"MinCropAreaConstraint_{c}_{l}_{y}_{s}\""
],
"outputs": [],
"execution_count": 32
},
{
"cell_type": "code",
"id": "bdf1048b83723f91",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:19.473674Z",
"start_time": "2024-09-08T08:14:19.412530Z"
}
},
"source": [
"# 条件13 ： 最重要的，不能重复种植同一块地同一年同一季的同一作物\n",
"# 每种作物在同一地块（含大棚）都不能连续重茬种植\n",
"\n",
"for l in LandName:\n",
" for c in CropName:\n",
" for y in years[1:]: # 单季不能连年种植 同一年不能连续种植 跨年也不能连续种植\n",
" if is_feasible(c, l, y - 1, 1) and is_feasible(c, l, y - 1, 2):\n",
" model += Y[c, l, y - 1, 1] + Y[c, l, y - 1, 2] <= 1, f\"RepeatPlantingConstraint_{c}_{l}_{y}\"\n",
" model += Y[c, l, y - 1, 2] + Y[c, l, y, 1] <= 1, f\"RepeatPlantingConstraint1_{c}_{l}_{y}\"\n",
" if is_feasible(c, l, y - 1, 1) and is_feasible(c, l, y, 1):\n",
" if l[0] in [\"A\", \"B\", \"C\"]: # and c in CropType[\"全粮食\"]:\n",
" model += Y[c, l, y - 1, 1] + Y[c, l, y, 1] <= 1, f\"RepeatPlantingConstraint2_{c}_{l}_{y}\"\n",
" elif l[0] == \"D\" and c == \"水稻\":\n",
" model += Y[c, l, y - 1, 1] + Y[c, l, y, 1] <= 1, f\"RepeatPlantingConstraint3_{c}_{l}_{y}\"\n"
],
"outputs": [],
"execution_count": 33
},
{
"cell_type": "code",
"id": "50da0aff9c68ae24",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:19.488304Z",
"start_time": "2024-09-08T08:14:19.474674Z"
}
},
"source": [
"# 条件14：从 2023 年开始，要求每个地块（含大棚） 在三年内至少种满一次豆类作物\n",
"for l in LandName:\n",
" for y in years[2:]: # 只需循环到倒数第三年，因为要考虑三年周期\n",
" # 在 y-2 到 y+2 之间的三年内，至少种满一次豆类作物\n",
" model += lpSum(X[c, l, y_r, s]\n",
" for c in CropType['豆类']\n",
" for y_r in range(y - 2, y + 1)\n",
" for s in SeasonNum if is_feasible(c, l, y_r, s)) >= LandArea[l], f\"BeanConstraint_{l}_{y}\"\n"
],
"outputs": [],
"execution_count": 34
},
{
"cell_type": "code",
"id": "d4792a1210afca3b",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:33.144757Z",
"start_time": "2024-09-08T08:14:19.489518Z"
}
},
"source": [
"%%time\n",
"model.solve(\n",
" pulp.GUROBI_CMD(msg=True,\n",
" timeLimit=7200,\n",
" threads=20,\n",
" options=[(\"Method\", 2), (\"MIPGap\", 0.0001)]))\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: total: 78.1 ms\n",
"Wall time: 13.6 s\n"
]
},
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 35
},
{
"cell_type": "code",
"id": "cb3f4e95111e3292",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:33.570095Z",
"start_time": "2024-09-08T08:14:33.146145Z"
}
},
"source": [
"# 输出文件名，包含起始年份和结束年份\n",
"output_filename = f\"planting_plan_1_1_{years[0]}-{years[-1]}.xlsx\"\n",
"\n",
"with pd.ExcelWriter(output_filename) as writer:\n",
" for y in years:\n",
"\n",
" # 用于存储需要交换的DataFrame\n",
" sheets_to_swap = {}\n",
"\n",
" for s in SeasonNum:\n",
" # 创建一个空的 DataFrame，横轴为作物名称，纵轴为地块名称\n",
" result_table = pd.DataFrame(columns=CropName, index=LandName)\n",
"\n",
" # 填充表格数据\n",
" for l in LandName:\n",
" for c in CropName:\n",
" # 获取变量 X[c, l, y, s] 的值\n",
" if is_feasible(c, l, y, s):\n",
" crop_value = value(X[c, l, y, s])\n",
" if crop_value is not None and crop_value > 0:\n",
" result_table.at[l, c] = crop_value # 填入结果\n",
"\n",
" # 用 0 填充空值\n",
" result_table.fillna(0, inplace=True)\n",
" sheet_name = f\"{y}_Season_第{s}季\"\n",
" result_table.to_excel(writer, sheet_name=sheet_name)\n",
"\n",
"print(f\"结果已保存到文件：{output_filename}\")\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"结果已保存到文件：planting_plan_1_1_2024-2030.xlsx\n"
]
}
],
"execution_count": 36
},
{
"cell_type": "code",
"id": "ebbf10d3",
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:33.943958Z",
"start_time": "2024-09-08T08:14:33.571680Z"
}
},
"source": [
"# 四舍五入版\n",
"output_filename = f\"planting_plan_1_1_{years[0]}-{years[-1]}-四舍五入版.xlsx\"\n",
"\n",
"with pd.ExcelWriter(output_filename) as writer:\n",
" for y in years:\n",
" # 用于存储需要交换的DataFrame\n",
" sheets_to_swap = {}\n",
"\n",
" for s in SeasonNum:\n",
" # 创建一个空的 DataFrame，横轴为作物名称，纵轴为地块名称\n",
" result_table = pd.DataFrame(columns=CropName, index=LandName)\n",
"\n",
" # 填充表格数据\n",
" for l in LandName:\n",
" for c in CropName:\n",
" # 获取变量 X[c, l, y, s] 的值\n",
" if is_feasible(c, l, y, s):\n",
" crop_value = value(X[c, l, y, s])\n",
" if crop_value is not None and crop_value > 0:\n",
" result_table.at[l, c] = crop_value # 填入结果\n",
"\n",
" # 用 0 填充空值\n",
" result_table.fillna(0, inplace=True)\n",
"\n",
" # 对DataFrame中的所有数值进行四舍五入，保留两位小数\n",
" result_table = result_table.round(2)\n",
"\n",
" sheet_name = f\"{y}_Season_第{s}季\"\n",
" result_table.to_excel(writer, sheet_name=sheet_name)\n"
],
"outputs": [],
"execution_count": 37
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T08:14:33.959313Z",
"start_time": "2024-09-08T08:14:33.944640Z"
}
},
"cell_type": "code",
"source": "",
"id": "ab1fd16df70c4293",
"outputs": [],
"execution_count": 37
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.17"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Reference in New Issue Copy Permalink