用python代码解决问题二

首先，为了解决问题二，我们需要创建一个简单的Python脚本来帮助做出决策。我们将编写函数来估算各种决策的成本和可能的结果。以下是代码的一个基础框架：

import numpy as np

# 定义函数，计算成本和收益
def calculate_costs_and_benefits(situations, detect_zero_part, detect_final_product, recycle):
    costs = {}
    for i, situation in enumerate(situations):
        # 零部件1和2的价格
        part1_price, part2_price = situation['price']
        
        # 成本计算
        if detect_zero_part[i]:
            # 检测零配件1的成本
            part1_detection_cost = situation['detection_cost'][0]
            # 检测零配件2的成本
            part2_detection_cost = situation['detection_cost'][1]
            total_detection_cost = part1_detection_cost + part2_detection_cost
            part1_rejected_rate = situation['reject_rate'][0]
            part2_rejected_rate = situation['reject_rate'][1]
            
            # 如果零配件都未检测，则直接进入装配
            if not detect_zero_part[i]:
                part1_rejected_rate *= 0
                part2_rejected_rate *= 0
            
            # 成品检测成本
            final_detection_cost = situation['final_detection_cost'] * (part1_rejected_rate or part2_rejected_rate)
            
            # 成品成本包括装配和最终检测
            production_cost = situation['assembly_cost'] + final_detection_cost
            costs[f'Situation {i}'] = {
                'production_cost': production_cost,
                'market_value': situation['market_value'],
                'recycling_cost': recycle[i] * production_cost if recycle[i] else 0,
                'total_cost': production_cost + recycling_cost
            }
        else:
            production_cost = situation['assembly_cost'] + situation['final_detection_cost']
            costs[f'Situation {i}'] = {
                'production_cost': production_cost,
                'market_value': situation['market_value'],
                'recycling_cost': 0,
                'total_cost': production_cost
            }

    return costs

# 表1的数据
situations = [
    {"price": (4, 2), "detection_cost": (2, 1), "reject_rate": (0.1, 0.1), "assembly_cost": 6, "final_detection_cost": 3, "market_value": 56, "recycle": 1},
    # 其他情况...
]

detect_zero_part = [True, False, True, False, True]  # 是否检测零配件
detect_final_product = [True, True, True, True, True]  # 是否检测成品
recycle = [False, True, True, True, False]  # 是否拆解并再利用

costs = calculate_costs_and_benefits(situations, detect_zero_part, detect_final_product, recycle)

for key, value in costs.items():
    print(f"Situation {key}:")
    print(f"\tTotal Cost: {value['total_cost']}")
    print(f"\tMarket Value: {value['market_value']}")
    print("\n")

# 你可以根据成本计算结果调整检测策略

这个代码只是一个基本的示例，你还需要根据具体情况调整输入参数，比如不同情况的具体数值。此外，你还可以增加更复杂的决策规则，如使用线性规划或动态规划算法来找到全局最优解。