车间调度算法python

在Python中，可以使用不同的算法来实现车间调度。下面是一些常见的车间调度算法的Python实现示例：

1. 贪心算法：

def greedy_scheduling(jobs):
    jobs = sorted(jobs, key=lambda x: x[1])  # 按工序的优先级进行排序
    schedule = []
    for job in jobs:
        start_time = max(job[0], schedule[-1][1]) if schedule else job[0]
        end_time = start_time + job[2]
        schedule.append((job[0], end_time))
    return schedule

2. 遗传算法：

import random

def genetic_scheduling(jobs, population_size, generations):
    population = [random.sample(jobs, len(jobs)) for _ in range(population_size)]  # 随机生成初始种群
    for _ in range(generations):
        fitness_scores = [fitness(job_order) for job_order in population]  # 计算每个个体的适应度
        elite_population = [population[i] for i in sorted(range(population_size), key=lambda x: fitness_scores[x], reverse=True)[:population_size // 2]]  # 选择适应度较高的个体作为精英个体
        offspring_population = []
        while len(offspring_population) < population_size - len(elite_population):
            parent1, parent2 = random.sample(elite_population, 2)  # 随机选择两个父代个体
            child = crossover(parent1, parent2)  # 交叉产生子代
            offspring_population.append(mutate(child))  # 随机变异子代
        population = elite_population + offspring_population
    best_job_order = max(population, key=lambda x: fitness(x))  # 选择适应度最高的个体作为最优解
    return best_job_order

def fitness(job_order):
    # 计算个体的适应度
    # 根据工序顺序计算总生产时间、成本等指标，并将其作为适应度评价
    pass

def crossover(parent1, parent2):
    # 交叉产生子代
    pass

def mutate(child):
    # 变异子代
    pass

3. 模拟退火算法：

import math
import random

def simulated_annealing_scheduling(jobs, initial_temperature, cooling_rate, num_iterations):
    current_solution = random.sample(jobs, len(jobs))  # 随机生成初始解
    best_solution = current_solution.copy()
    current_cost = calculate_cost(current_solution)  # 计算当前解的成本
    best_cost = current_cost

    for i in range(num_iterations):
        temperature = initial_temperature / (1 + cooling_rate * i)  # 更新温度
        new_solution = get_neighbor(current_solution)  # 获得当前解的邻居解
        new_cost = calculate_cost(new_solution)  # 计算邻居解的成本