{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "e19ec4ae5347c678", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "pd.options.display.max_columns = 100" ] }, { "cell_type": "code", "execution_count": 2, "id": "initial_id", "metadata": {}, "outputs": [], "source": [ "\n", "df_crop_details = pd.read_excel('./data/2.xlsx', sheet_name=1)" ] }, { "cell_type": "code", "execution_count": 3, "id": "1d2a51b3414be94", "metadata": {}, "outputs": [], "source": [ "# 去除空格\n", "df_crop_details['cropName'] = df_crop_details['cropName'].apply(lambda x: x.strip())\n", "# CropType = [x.strip() for x in CropType]\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "514cd9136d9ca341", "metadata": {}, "outputs": [], "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()\n", "# df_crop_planting" ] }, { "cell_type": "code", "execution_count": 5, "id": "1503f8b642c842db", "metadata": {}, "outputs": [], "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())\n", "# df_land" ] }, { "cell_type": "code", "execution_count": 6, "id": "3cdf51a9a9d4d30f", "metadata": {}, "outputs": [], "source": [ "\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\n", "# df_crop_details" ] }, { "cell_type": "code", "execution_count": 7, "id": "569016a9b90f841b", "metadata": {}, "outputs": [], "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())\n", "# df_crop_type_land" ] }, { "cell_type": "code", "execution_count": 8, "id": "a7661d84217b578c", "metadata": {}, "outputs": [], "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]" ] }, { "cell_type": "code", "execution_count": 9, "id": "4a4e06a1f2d4d8ab", "metadata": {}, "outputs": [], "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\"}\n", "# LandType" ] }, { "cell_type": "code", "execution_count": 10, "id": "cc938565a63a8129", "metadata": {}, "outputs": [], "source": [ "# 枚举地块\n", "LandName = df_crop_planting['landName'].unique()\n", "# LandName" ] }, { "cell_type": "code", "execution_count": 11, "id": "dae53a6215cea525", "metadata": {}, "outputs": [], "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}\n", "# LandArea" ] }, { "cell_type": "code", "execution_count": 12, "id": "7a0e6cc209d93b56", "metadata": {}, "outputs": [], "source": [ "# 读入作物名称\n", "CropName = df_crop_details['cropName'].unique()\n", "# CropName" ] }, { "cell_type": "code", "execution_count": 13, "id": "8a280f918bb3139a", "metadata": {}, "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)}" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "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" ] }, { "cell_type": "code", "execution_count": 14, "id": "bf6d61d66f06bdeb", "metadata": {}, "outputs": [], "source": [ "# any([x[-1] == ' ' for x in CropName])\n", "# 居然有空格\n", "# 现在去掉了" ] }, { "cell_type": "code", "execution_count": 15, "id": "22f9730e3d9c1016", "metadata": {}, "outputs": [], "source": [ "# 亩产量\n", "unit_yield_lsc = {\n", " l: {\n", " s: {\n", " c: df_crop_details[\n", " (df_crop_details['cropName'] == c) &\n", " (df_crop_details['cropLandType'] == l) &\n", " (df_crop_details['season'] == s)\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'] == s)\n", " ]['unitYield'].values.size > 0 else 0\n", " for c in CropName\n", " } for s in SeasonType\n", " }\n", " for l in LandType.values()\n", "}\n", "\n", "# unit_yield" ] }, { "cell_type": "code", "execution_count": 16, "id": "99dbe4ca6c54db0b", "metadata": {}, "outputs": [], "source": [ "# 亩利润\n", "unit_profit_lsc = {\n", " l: {\n", " s: {\n", " c: df_crop_details[\n", " (df_crop_details['cropName'] == c) &\n", " (df_crop_details['cropLandType'] == l) &\n", " (df_crop_details['season'] == s)\n", " ]['unitProfit'].values[0]\n", " if df_crop_details[\n", " (df_crop_details['cropName'] == c) &\n", " (df_crop_details['cropLandType'] == l) &\n", " (df_crop_details['season'] == s)\n", " ]['unitYield'].values.size > 0 else 0\n", " for c in CropName\n", " } for s in SeasonType\n", " }\n", " for l in LandType.values()\n", "}\n", "\n", "# unit_profit" ] }, { "cell_type": "code", "execution_count": 17, "id": "13f2ccc7aa215d4c", "metadata": {}, "outputs": [], "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]]][line[5]][line[2]]\n", "\n", "# crop_demand" ] }, { "cell_type": "code", "execution_count": 18, "id": "5d09872708d40da4", "metadata": {}, "outputs": [], "source": [ "# 准备开搞\n", "from pulp import LpMaximize, LpProblem, LpVariable, lpSum, value, LpInteger, PULP_CBC_CMD, LpContinuous, LpBinary\n", "import pulp" ] }, { "cell_type": "code", "execution_count": 19, "id": "b6eff87c7a762769", "metadata": {}, "outputs": [], "source": [ "model = LpProblem(\"Crop_Planting_Optimization_with_Specific_Rules\", LpMaximize)" ] }, { "cell_type": "code", "execution_count": 20, "id": "7bfdb9425c3e9683", "metadata": {}, "outputs": [], "source": [ "# %timeit X = LpVariable.dicts(\"X\", (CropName, LandName, years, seasons), lowBound=0, cat=LpContinuous)\n", "# %timeit XX = LpVariable.dicts(\"X\", [(c, l, y, s) for c in CropName for l in LandName for y in years for s in seasons],lowBound=0, cat=LpContinuous)" ] }, { "cell_type": "code", "execution_count": 21, "id": "4cb129e20385a7ee", "metadata": {}, "outputs": [], "source": [ "# 地块l在y年的s季的种植c的量\n", "# X = LpVariable.dicts(\"crop_plant_area\", (CropName, LandName, years, SeasonType), lowBound=0, cat=LpContinuous)\n", "X = LpVariable.dicts(\"X\", [(c, l, y, s)\n", " for c in CropName\n", " for l in LandName\n", " for y in years\n", " for s in SeasonType],\n", " lowBound=0,\n", " cat=LpContinuous\n", " )\n", "# X" ] }, { "cell_type": "code", "execution_count": 22, "id": "9dab983ca7b3b607", "metadata": {}, "outputs": [], "source": [ "# 地块l在y年的s季是否种植了c\n", "# Y = LpVariable.dicts(\"crop_plant_bool\", (CropName, LandName, years, SeasonType), cat=LpBinary)\n", "Y = LpVariable.dicts(\"Y\", [(c, l, y, s) for c in CropName for l in LandName for y in years for s in SeasonType],\n", " cat=LpBinary)\n", "\n", "# Y" ] }, { "cell_type": "code", "execution_count": 23, "id": "603175d36624172f", "metadata": {}, "outputs": [], "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 SeasonType:\n", " # 如果种植了作物，则种植面积大于0\n", " model += X[c, l, y, s] <= 10000000 * 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] >= 0.000001 * Y[c, l, y, s], f\"PlantingConstraint2_{c}_{l}_{y}_{s}\"" ] }, { "cell_type": "code", "execution_count": 24, "id": "4505b0f2fe6bf93d", "metadata": {}, "outputs": [], "source": [ "# 目标函数：总利润 = sum(x[i]*profit[c,l,s])\n", "model += lpSum( # 地块类型 季节 作物类型\n", " X[c, l, y, s] * unit_profit_lsc[LandType[l[0]]][s][c]\n", " for c in CropName\n", " for l in LandName\n", " for y in years\n", " for s in SeasonType\n", "), \"total_profit\"\n" ] }, { "cell_type": "code", "execution_count": 25, "id": "1f185c0856fbcf27", "metadata": {}, "outputs": [], "source": [ "# 约束，启动！\n", "# 条件1：每年的所有季节中每种作物的种植量必须小于需求量\n", "for c in CropName:\n", " for y in years:\n", " model += lpSum(X[c, l, y, s] * unit_yield_lsc[LandType[l[0]]][s][c] for l in LandName for s in SeasonType) <= \\\n", " crop_demand[c], f\"CropDemandConstraint_{c}_{y}\"" ] }, { "cell_type": "code", "execution_count": 26, "id": "7e13de7700d38f19", "metadata": {}, "outputs": [], "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 SeasonType:\n", " model += lpSum(X[c, l, y, s] for c in CropName) <= LandArea[l], f\"LandAreaConstraint_{l}_{y}_{s}\"" ] }, { "cell_type": "code", "execution_count": 27, "id": "1f6ac4323825f28b", "metadata": {}, "outputs": [], "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的产量和利润的0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] not in [\"A\", \"B\", \"C\"]: # [\"平旱地\", \"梯田\", \"山坡地\"]\n", " continue\n", " for s in SeasonType:\n", " if s != \"单季\":\n", " model += lpSum(Y[c, l, y, s] for c in CropName for y in years) == 0, f\"SingleSeasonConstraint_{l}_{s}\"\n", " # model += lpSum(X[c, l, y, s] for c in CropName for y in years) == 0" ] }, { "cell_type": "code", "execution_count": 28, "id": "c0e9b6b37092f0e7", "metadata": {}, "outputs": [], "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\": # [\"水浇地\"]\n", " continue\n", " for y in years:\n", " model += (\n", " (lpSum(Y[\"水稻\", l, y, \"单季\"]) != 0) and\n", " (lpSum(\n", " Y[c, l, y, s] if c != \"水稻\" else 0\n", " for c in CropName\n", " for s in [\"第一季\", \"第二季\"]\n", " ) == 0)\n", " ) or (\n", " (lpSum(Y[\"水稻\", l, y, \"单季\"]) == 0) and\n", " (lpSum(\n", " Y[c, l, y, s] if c != \"水稻\" else 0\n", " for c in CropName\n", " for s in [\"第一季\", \"第二季\"]\n", " ) != 0)\n", " ), f\"irrigatedConstraint_{l}_{y}\"\n", "\n" ] }, { "cell_type": "code", "execution_count": 29, "id": "8f8da502641f32fa", "metadata": {}, "outputs": [], "source": [ "# 条件5： 若在某块水浇地种植两季蔬菜，第一季可种植多种蔬菜（大白菜、白萝卜和红萝卜除外）；第二季只能种植大白菜、白萝卜和红萝卜中的一种（便于管理）。\n", "# 感觉没必要，因为前面有field和profit为0的惩罚\n" ] }, { "cell_type": "code", "execution_count": 30, "id": "df2d7e8918382a0", "metadata": {}, "outputs": [], "source": [ "# 条件6： 根据季节性要求，大白菜、白萝卜和红萝卜只能在水浇地的第二季种植。\n", "# 感觉也没必要，同理\n" ] }, { "cell_type": "code", "execution_count": 31, "id": "1d402d3df13f6732", "metadata": {}, "outputs": [], "source": [ "# 条件7： 普通大棚每年种植两季作物，第一季可种植多种蔬菜（大白菜、白萝卜和红萝卜除外），第二季只能种植食用菌。\n", "# 同理\n" ] }, { "cell_type": "code", "execution_count": 32, "id": "930ab378623172ec", "metadata": {}, "outputs": [], "source": [ "# 条件8： 食用菌类只能在普通大棚第二季的时候种植\n", "# 同理\n" ] }, { "cell_type": "code", "execution_count": 33, "id": "bda8b4c538a097bc", "metadata": {}, "outputs": [], "source": [ "# 条件9： 智慧大棚每年都可种植两季蔬菜（大白菜、白萝卜和红萝卜除外）\n", "# 同理" ] }, { "cell_type": "code", "execution_count": 29, "id": "e8fb2dea1326a05b", "metadata": {}, "outputs": [], "source": [ "# 条件10： 别种得太碎了，啥意思？ 每种作物不超过3块地吧。\n", "for c in CropName:\n", " for y in years:\n", " for s in SeasonType:\n", " model += lpSum(Y[c, l, y, s] for l in LandName) <= 3, f\"MaxCropTypeConstraint_{c}_{y}_{s}\"" ] }, { "cell_type": "code", "execution_count": 30, "id": "49f1490823c6af3c", "metadata": {}, "outputs": [], "source": [ "# 条件11： 最小种植面积：0.2吧\n", "for l in LandName:\n", " for y in years:\n", " for s in SeasonType:\n", " for c in CropName:\n", " model += X[c, l, y, s] >= 0.2 * (1 if Y[c, l, y, s] else 0), f\"MinCropAreaConstraint_{c}_{l}_{y}_{s}\"" ] }, { "cell_type": "code", "execution_count": 31, "id": "bdf1048b83723f91", "metadata": {}, "outputs": [], "source": [ "# 条件12 ： 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: # 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 y < years[-1]:\n", " model += Y[c, l, y, \"单季\"] + Y[c, l, y + 1, \"单季\"] <= 1, f\"SingleSeasonConstraint1_{c}_{l}_{y}\"\n", " model += Y[c, l, y, \"第一季\"] + Y[c, l, y + 1, \"第二季\"] <= 1, f\"DoubleSeasonConstraint1_{c}_{l}_{y}\"\n", " model += Y[c, l, y, \"第一季\"] + Y[c, l, y, \"第二季\"] <= 1, f\"DoubleSeasonConstraint2_{c}_{l}_{y}\"\n" ] }, { "cell_type": "code", "execution_count": 37, "id": "50da0aff9c68ae24", "metadata": {}, "outputs": [], "source": [ "# 条件13： 最重要的，从 2023 年开始要求每个地块（含大棚） 的所有土地三年内至少种植一次豆类作物\n", "# CropType['豆类']\n", "for l in LandName:\n", " for y in years[:-2]:\n", " model += lpSum(Y[c,l,y_r,s] for c in CropType['豆类'] for y_r in range(y,y+2) for s in SeasonType)>=1, f\"BeanConstraint_{l}_{y}\"\n", " # model += lpSum(Y[c, l, y_r, \"单季\"] for c in CropType['豆类'] for y_r in\n", " # range(y - 2, y + 1)) >= 1, f\"BeanConstraint1_{l}_{y}\"\n", " # model += lpSum(Y[c, l, y_r, s] for c in CropType['豆类'] for y_r in range(y - 2, y + 1) for s in\n", " # [\"第一季\", \"第二季\"]) >= 1, f\"BeanConstraint2_{l}_{y}\"" ] }, { "cell_type": "code", "execution_count": 32, "id": "d4792a1210afca3b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# model.solve()\n", "\n", "# 创建求解器实例\n", "solver = pulp.PULP_CBC_CMD(\n", " timeLimit=120,\n", " threads=4,\n", ")\n", "solver.solve(model)\n", "\n", "\n", "# print(Y)\n", "# for c in CropName:\n", "# for l in LandName:\n", "# for y in years:\n", "# for s in SeasonType:\n", "# print(f\"Y[({c}, {l}, {y}, {s})] = {pulp.value(Y[(c, l, y, s)])}\")\n" ] }, { "cell_type": "code", "execution_count": 40, "id": "cb3f4e95111e3292", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "结果已保存到文件：planting_plan_2024-2030.xlsx\n" ] } ], "source": [ "\n", "# 输出文件名，包含起始年份和结束年份\n", "output_filename = f\"planting_plan_{years[0]}-{years[-1]}.xlsx\"\n", "\n", "# 使用 pd.ExcelWriter 创建 Excel 文件\n", "with pd.ExcelWriter(output_filename) as writer:\n", " # 遍历每一年和每个季节\n", " for y in years:\n", " for s in SeasonType:\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", " 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", " # 定义每个 sheet 的名称为 年份_季节\n", " sheet_name = f\"{y}_Season_{s}\"\n", "\n", " # 将每个 result_table 写入到 Excel 的不同 sheet 中\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" ] }, { "cell_type": "code", "execution_count": null, "id": "93b4b3b64fd941aa", "metadata": {}, "outputs": [], "source": [] } ], "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 }