给我提供一个第一类装配线平衡问题python代码

在Python中处理第一类装配线平衡问题通常涉及优化任务分配，确保每个工作站的工作量尽可能均衡，以便提高生产效率。这是一个经典的组合优化问题，可以使用遗传算法、模拟退火等算法解决。下面是一个简单的例子，使用模拟退火算法求解：

import numpy as np

# 假设我们有n个工作站和m件产品
def fitness_function(workload):
    # 这里只是一个简化版本，实际计算应考虑工作强度、机器效率等因素
    return sum(workload)

def swap(solution):
    # 随机选择两个元素交换位置
    i = np.random.randint(0, len(solution) - 1)
    j = np.random.randint(i + 1, len(solution))
    solution[i], solution[j] = solution[j], solution[i]
    return solution

def annealing(initial_solution, temp=1000, cooling_rate=0.95, max_iter=1000):
    current_solution = initial_solution.copy()
    best_solution = initial_solution
    best_fitness = fitness_function(current_solution)
    
    for _ in range(max_iter):
        new_solution = swap(current_solution)
        new_fitness = fitness_function(new_solution)
        
        if new_fitness < best_fitness:
            best_solution = new_solution
            best_fitness = new_fitness
        
        acceptance_prob = np.exp((best_fitness - new_fitness) / temp)
        if np.random.rand() < acceptance_prob:
            current_solution = new_solution
            
        temp *= cooling_rate
    
    return best_solution, best_fitness

# 示例：假设我们有5个工作站和10件产品
n_workstations = 5
products = list(range(1, n_workstations + 1))

# 初始化一个随机解决方案
initial_solution = np.random.permutation(products)

optimal_solution, optimal_fitness = annealing(initial_solution)
print(f"最优解：{optimal_solution}, 最优值：{optimal_fitness}")