from pulp import *

# データセットの設定
N = 5  # 購入可能な最大のアイテム数
B = 100  # 予算
items = [
    {'name': 'テーブル', 'cost': 30, 'importance': 8},
    {'name': '椅子', 'cost': 20, 'importance': 6},
    {'name': '照明器具', 'cost': 50, 'importance': 9},
    {'name': '本棚', 'cost': 40, 'importance': 7},
    {'name': 'ベッド', 'cost': 60, 'importance': 10}
]

# 問題の定義
prob = LpProblem("MinimalistFurnitureProblem", LpMaximize)

# 変数の作成
x = LpVariable.dicts("Item", range(len(items)), lowBound=0, upBound=1, cat=LpInteger)

# 目的関数の定義
prob += lpSum([items[i]['importance'] * x[i] for i in range(len(items))])

# 制約条件の定義
prob += lpSum([items[i]['cost'] * x[i] for i in range(len(items))]) <= B
prob += lpSum(x) <= N

# 問題の解決
prob.solve()

# 結果の出力
print("Status:", LpStatus[prob.status])
print("最適なアイテムリスト:")
for i in range(len(items)):
    if value(x[i]) > 0:
        print(f"{items[i]['name']}: 重要度 {items[i]['importance']}, コスト {items[i]['cost']}")

from pulp import *

# 問題の定義
problem = LpProblem("Career Optimization", LpMaximize)

# 企業の情報
companies = {
    "A": {"salary": 500, "satisfaction": 100},
    "B": {"salary": 600, "satisfaction": 90},
    "C": {"salary": 400, "satisfaction": 120}
}

# 変数の作成
choices = LpVariable.dicts("Choice", companies, cat="Binary")

# 目的関数の定義
problem += lpSum(choices[company] * (companies[company]["salary"] + companies[company]["satisfaction"]) for company in companies)

# 制約条件の定義
problem += lpSum(choices.values()) == 1  # 1社だけを選択する

# 問題の解決
problem.solve()

# 結果の出力
print("最適解:")
for company in companies:
    if choices[company].varValue == 1:
        print(f"{company}社を選択する")

from pulp import LpProblem, LpVariable, LpInteger, LpMinimize, lpSum, value

problem = LpProblem("Election Survey", LpMinimize)

# 選挙区の数
num_districts = 5

# 調査所の数
num_polling_stations = 3

# 調査費用
survey_costs = [10, 15, 12]

# 各選挙区の調査人数の制約
min_surveyors = [2, 3, 1, 2, 2]
max_surveyors = [5, 6, 4, 5, 4]

# 変数の作成
x = {}
for i in range(num_polling_stations):
    for j in range(num_districts):
        x[i, j] = LpVariable(f"x_{i}_{j}", lowBound=0, cat=LpInteger)

# 目的関数（調査費用の最小化）
problem += lpSum(survey_costs[i] * x[i, j] for i in range(num_polling_stations) for j in range(num_districts))

# 各選挙区には少なくとも1つの調査所が必要
for j in range(num_districts):
    problem += lpSum(x[i, j] for i in range(num_polling_stations)) >= 1

# 各選挙区の調査人数の制約
for j in range(num_districts):
    problem += lpSum(x[i, j] for i in range(num_polling_stations)) >= min_surveyors[j]
    problem += lpSum(x[i, j] for i in range(num_polling_stations)) <= max_surveyors[j]

problem.solve()

for i in range(num_polling_stations):
    for j in range(num_districts):
        print(f"Polling Station {i+1} in District {j+1}: {value(x[i, j])}")

from pulp import LpProblem, LpVariable, LpInteger, LpMinimize, lpSum, value

# 選挙区の数
num_districts = 5

# 調査所の数
num_polling_stations = 3

# 調査費用
survey_costs = [10, 15, 12]

# 各選挙区の調査人数の制約
min_surveyors = [2, 3, 1, 2, 2]
max_surveyors = [5, 6, 4, 5, 4]

# 問題の作成
problem = LpProblem("Election Survey", LpMinimize)

# 変数の作成
x = {}
for i in range(num_polling_stations):
    for j in range(num_districts):
        x[i, j] = LpVariable(f"x_{i}_{j}", lowBound=0, cat=LpInteger)

# 目的関数（調査費用の最小化）
problem += lpSum(survey_costs[i] * x[i, j] for i in range(num_polling_stations) for j in range(num_districts))

# 各選挙区には少なくとも1つの調査所が必要
for j in range(num_districts):
    problem += lpSum(x[i, j] for i in range(num_polling_stations)) >= 1

# 各選挙区の調査人数の制約
for j in range(num_districts):
    problem += lpSum(x[i, j] for i in range(num_polling_stations)) >= min_surveyors[j]
    problem += lpSum(x[i, j] for i in range(num_polling_stations)) <= max_surveyors[j]

# 問題を解く
problem.solve()

# 最適解を出力
print("Optimization Status:", problem.status)
print("Minimum Survey Cost:", value(problem.objective))

# 各調査所の割り当て結果を表示
for i in range(num_polling_stations):
    for j in range(num_districts):
        print(f"Polling Station {i+1} in District {j+1}: {value(x[i, j])}")

from pulp import *

# 問題の定義
problem = LpProblem("War Transportation Problem", LpMinimize)

# 変数の定義
base_names = ["Base1", "Base2", "Base3"]  # 自国の拠点
enemy_bases = ["Enemy1", "Enemy2", "Enemy3"]  # 敵地の拠点

# 輸送量変数
transport_vars = LpVariable.dicts("Transport", (base_names, enemy_bases), lowBound=0, cat="Integer")

# 輸送コスト辞書の定義
transport_cost_dict = {
    "Base1": {
        "Enemy1": 10,
        "Enemy2": 8,
        "Enemy3": 6
    },
    "Base2": {
        "Enemy1": 9,
        "Enemy2": 7,
        "Enemy3": 5
    },
    "Base3": {
        "Enemy1": 11,
        "Enemy2": 9,
        "Enemy3": 7
    }
}

# 目的関数の定義
problem += lpSum(transport_vars[base][enemy] * transport_cost_dict[base][enemy]
                 for base in base_names for enemy in enemy_bases)

# 制約条件の定義
# 各自国の拠点からの輸送量は、自国の拠点の供給量以下である必要がある
supply_dict = {"Base1": 100, "Base2": 150, "Base3": 200}
for base in base_names:
    problem += lpSum(transport_vars[base][enemy] for enemy in enemy_bases) <= supply_dict[base]

# 各敵地の拠点への輸送量は、敵地の拠点の需要量以上である必要がある
demand_dict = {"Enemy1": 80, "Enemy2": 120, "Enemy3": 100}
for enemy in enemy_bases:
    problem += lpSum(transport_vars[base][enemy] for base in base_names) >= demand_dict[enemy]

# 最適化の実行
problem.solve()

# 結果の出力
print("Optimal Solution:")
for base in base_names:
    for enemy in enemy_bases:
        print(f"Transport from {base} to {enemy}: {transport_vars[base][enemy].varValue}")

from pulp import *

# 栄養価と価格のデータ
food = ['apple', 'banana', 'beef', 'chicken', 'milk']
cost = {'apple': 0.5, 'banana': 0.25, 'beef': 2.5, 'chicken': 1.5, 'milk': 0.4}
calories = {'apple': 50, 'banana': 100, 'beef': 250, 'chicken': 150, 'milk': 120}
protein = {'apple': 0.5, 'banana': 1.0, 'beef': 25, 'chicken': 30, 'milk': 8}
fat = {'apple': 0.2, 'banana': 0.4, 'beef': 18, 'chicken': 5, 'milk': 5}
carbs = {'apple': 13, 'banana': 27, 'beef': 0, 'chicken': 0, 'milk': 12}

# 目標とする栄養素の量
target_calories = 1500
target_protein = 100
target_fat = 50
target_carbs = 200

# 問題を定義する
prob = LpProblem("diet problem", LpMinimize)

# 各食品の摂取量を表す変数を定義する
food_vars = LpVariable.dicts("food", food, lowBound=0, cat='Continuous')

# 目的関数を定義する
prob += lpSum([cost[f] * food_vars[f] for f in food]), "Total Cost"

# 制約条件を定義する
prob += lpSum([calories[f] * food_vars[f] for f in food]) >= target_calories, "Calories Minimum"
prob += lpSum([calories[f] * food_vars[f] for f in food]) <= target_calories + 100, "Calories Maximum"
prob += lpSum([protein[f] * food_vars[f] for f in food]) >= target_protein, "Protein Minimum"
prob += lpSum([fat[f] * food_vars[f] for f in food]) >= target_fat, "Fat Minimum"
prob += lpSum([carbs[f] * food_vars[f] for f in food]) >= target_carbs, "Carbs Minimum"

# 問題を解く
prob.solve()

# 結果を表示する
print("Status:", LpStatus[prob.status])
for v in prob.variables():
    print(v.name, "=", v.varValue)
print("Total Cost =", value(prob.objective))

from pulp import *

# 最大の住宅ローン控除額
H = 500000

# 最大の教育費控除額
E = 100000

# 最大の寄附金控除額
D = 30000

# 総支払額
T = 1000000

# 問題を定義
prob = LpProblem("tax_optimization", LpMinimize)

# 変数を定義
h = LpVariable("h", 0, H)
e = LpVariable("e", 0, E)
d = LpVariable("d", 0, D)

# 目的関数を定義
prob += T - (h + e + d)

# 制約条件を定義
prob += h <= H
prob += e <= E
prob += d <= D
prob += h + e + d <= T

# 最適化を実行
status = prob.solve()

# 結果を表示
print(f"Status: {LpStatus[status]}")
print(f"Optimal value: {value(prob.objective)}")
print(f"h: {value(h.value())}, e: {value(e.value())}, d: {value(d.value())}")

import pulp

# 問題の定義
problem = pulp.LpProblem('school_lunch', pulp.LpMinimize)

# 変数の定義
a = pulp.LpVariable('a', lowBound=0, cat='Continuous')
b = pulp.LpVariable('b', lowBound=0, cat='Continuous')
c = pulp.LpVariable('c', lowBound=0, cat='Continuous')

# 目的関数の定義
problem += 2 * a + 3 * b + 4 * c

# 制約条件の定義
problem += 700 * a + 600 * b + 800 * c >= 10000
problem += 30 * a + 25 * b + 20 * c >= 500
problem += 10 * a + 15 * b + 20 * c >= 200
problem += 50 * a + 40 * b + 60 * c >= 700

# 問題の解法
status = problem.solve()

# 解の出力
print('Result:')
print(f'a: {pulp.value(a)}')
print(f'b: {pulp.value(b)}')
print(f'c: {pulp.value(c)}')
print(f'Minimum Cost: {pulp.value(problem.objective)}')

from pulp import *

# 問題の定義
prob = LpProblem('Onigokko', LpMaximize)

# 変数の定義
x = [LpVariable('x{}'.format(i), cat='Binary') for i in range(1, 6)]

# 目的関数の定義
prob += lpSum(x)

# 制約条件の定義
prob += lpSum(x) <= 4

# 求解
status = prob.solve()

# 結果を表示
print('Status:', LpStatus[status])
for i in range(1, 6):
    print('x{} = {}'.format(i, int(value(x[i - 1]))))
print('Optimal value:', value(prob.objective))

# ①必要なライブラリをインポート
from pulp import *

# ②問題を定義する
prob = LpProblem('出店最適化', LpMaximize)

# ③変数を定義する
x1 = LpVariable('場所1', 0, 1, LpInteger)
x2 = LpVariable('場所2', 0, 1, LpInteger)
x3 = LpVariable('場所3', 0, 1, LpInteger)

# ④目的関数を定義する
prob += 70 * x1 + 50 * x2 + 80 * x3

# ⑤制約条件を定義する
prob += x1 + x2 + x3 == 1
prob += 90 * x1 + 50 * x2 + 70 * x3 <= 100
prob += 100 * x1 + 50 * x2 + 70 * x3 <= 100

# ⑥最適化を実行する
status = prob.solve()

# ⑦結果を表示する
print('出店する場所:', end=' ')
if x1.value() == 1:
    print('場所1')
elif x2.value() == 1:
    print('場所2')
elif x3.value() == 1:
    print('場所3')

print('最適な評価スコア:', value(prob.objective))

from pulp import *

# 変数の定義
x1 = LpVariable("x1", lowBound=0)
x2 = LpVariable("x2", lowBound=0)
x3 = LpVariable("x3", lowBound=0)

# 目的関数の定義
prob = LpProblem("Prescription", LpMaximize)
prob += 2*x1 + 3*x2 + 4*x3

# 制約条件の定義
prob += x1 + x2 + x3 <= 1000
prob += 0.5 * x1 + x2 + x3 <= 800
prob += x1 + x2 + 2 * x3 <= 1500

# 問題を解く
status = prob.solve()

# 結果の出力
print("Status:", LpStatus[status])
print("Optimal Solution:")
for v in prob.variables():
    print(v.name, "=", v.varValue)
print("Optimal Value =", value(prob.objective))