用python编写一个求解将n个工件分配给f个工厂，每个工厂有i个生产阶段，每个生产阶段有m个并行机器，每个工件安排好工厂后只能在该工厂加工，直至加工完成，每台机器在工作时和空闲时有不同的能耗，总目标是最小化完工时间和最小化总能耗的调度问题的算法，

这是一个比较复杂的调度问题，可以使用遗传算法或模拟退火算法等优化算法进行求解。以下是一个基于遗传算法的示例代码：

import random

# 工件数量
n = 10

# 工厂数量
f = 3

# 每个工厂的生产阶段数量
phases = [2, 3, 2]

# 每个生产阶段的并行机器数量
machines = [[3, 4], [2, 3, 4], [3, 5]]

# 每台机器在工作时和空闲时的能耗
energy = [[1, 2], [2, 3, 4], [3, 4]]

# 生成随机解
def generate_random_solution():
    solution = []
    for i in range(n):
        factory = random.randint(0, f-1)
        phase = random.randint(0, phases[factory]-1)
        machine = random.randint(0, machines[factory][phase]-1)
        solution.append((factory, phase, machine))
    return solution

# 计算解的总时间和总能耗
def calculate_fitness(solution):
    completion_time = [0] * f
    energy_consumption = 0
    for i in range(n):
        factory, phase, machine = solution[i]
        time = completion_time[factory]
        time = max(time, phase)
        time += energy[machine][0]
        completion_time[factory] = time
        energy_consumption += energy[machine][0] if time == phase else energy[machine][1]
    return (max(completion_time), energy_consumption)

# 交叉操作
def crossover(parent1, parent2):
    crossover_point = random.randint(1, n-1)
    child1 = parent1[:crossover_point] + parent2[crossover_point:]
    child2 = parent2[:crossover_point] + parent1[crossover_point:]
    return child1, child2

# 变异操作
def mutate(solution):
    mutation_point = random.randint(0, n-1)
    factory = random.randint(0, f-1)
    phase = random.randint(0, phases[factory]-1)
    machine = random.randint(0, machines[factory][phase]-1)
    solution[mutation_point] = (factory, phase, machine)
    return solution

# 遗传算法求解
population_size = 100
elite_size = 10
mutation_rate = 0.1
generations = 1000

population = [generate_random_solution() for i in range(population_size)]

for i in range(generations):
    fitness_scores = [calculate_fitness(solution) for solution in population]
    sorted_population = [x for _, x in sorted(zip(fitness_scores, population))]
    next_population = sorted_population[:elite_size]

    while len(next_population) < population_size:
        parent1 = random.choice(sorted_population[:population_size//2])
        parent2 = random.choice(sorted_population[:population_size//2])
        child1, child2 = crossover(parent1, parent2)
        if random.random() < mutation_rate:
            child1 = mutate(child1)
        if random.random() < mutation_rate:
            child2 = mutate(child2)
        next_population.extend([child1, child2])

    population = next_population

best_solution = sorted_population[0]
best_fitness = calculate_fitness(best_solution)

print("Best solution:", best_solution)
print("Best fitness:", best_fitness)

该代码中使用了遗传算法对问题进行求解。首先，定义了工件数量、工厂数量、每个工厂的生产阶段数量、每个生产阶段的并行机器数量和每台机器在工作时和空闲时的能耗等参数。然后，定义了生成随机解、计算解的总时间和总能耗、交叉操作、变异操作等函数。最后，使用遗传算法求解最优解，并输出结果。

需要注意的是，该代码只是一个示例，实际问题中可能需要根据具体情况进行修改和优化。