gui界面遗传算法求解流水车间调度问题设计 python

遗传算法是一种常用的优化算法，可以用于求解流水车间调度问题。下面是一个使用 Python 的 Tkinter 模块设计的 GUI 界面，可以通过遗传算法求解流水车间调度问题：

import tkinter as tk
import random
import numpy as np


def init_population(pop_size, job_count, machine_count):
    # 初始化种群
    population = []
    for i in range(pop_size):
        chromosome = np.zeros((job_count, machine_count), dtype=int)
        for j in range(job_count):
            chromosome[j] = np.random.permutation(machine_count)
        population.append(chromosome)
    return population


def fitness(chromosome, processing_times):
    # 计算适应度
    makespan = np.zeros(len(chromosome), dtype=int)
    for i in range(len(chromosome)):
        job_times = np.zeros(len(chromosome[i]), dtype=int)
        for j in range(len(chromosome[i])):
            machine = chromosome[i][j]
            job_times[machine] += processing_times[i][machine]
            if job_times[machine] > makespan[i]:
                makespan[i] = job_times[machine]
    return 1.0 / makespan


def selection(population, fitness_values):
    # 选择操作
    fitness_sum = sum(fitness_values)
    probabilities = [fitness_values[i] / fitness_sum for i in range(len(fitness_values))]
    selected_indices = np.random.choice(len(population), size=len(population), p=probabilities)
    selected_population = [population[i] for i in selected_indices]
    return selected_population


def crossover(parent1, parent2):
    # 交叉操作
    child1 = parent1.copy()
    child2 = parent2.copy()
    crossover_point = int(len(parent1) / 2)
    child1[crossover_point:] = parent2[crossover_point:]
    child2[crossover_point:] = parent1[crossover_point:]
    return child1, child2


def mutation(chromosome, mutation_prob):
    # 变异操作
    for i in range(len(chromosome)):
        for j in range(len(chromosome[i])):
            if random.random() < mutation_prob:
                chromosome[i][j] = np.random.randint(len(chromosome[i]))
    return chromosome


def genetic_algorithm(pop_size, job_count, machine_count, processing_times, max_generations):
    # 遗传算法求解流水车间调度问题
    population = init_population(pop_size, job_count, machine_count)
    for generation in range(max_generations):
        fitness_values = [fitness(chromosome, processing_times) for chromosome in population]
        selected_population = selection(population, fitness_values)
        new_population = []
        for i in range(int(pop_size / 2)):
            parent1 = random.choice(selected_population)
            parent2 = random.choice(selected_population)
            child1, child2 = crossover(parent1, parent2)
            child1 = mutation(child1, 0.1)
            child2 = mutation(child2, 0.1)
            new_population.append(child1)
            new_population.append(child2)
        population = new_population
    best_chromosome = max(population, key=lambda c: fitness(c, processing_times))
    return best_chromosome


class Application(tk.Frame):
    def __init__(self, master=None):
        super().__init__(master)
        self.master = master
        self.pack()
        self.create_widgets()

    def create_widgets(self):
        # 创建控件
        self.job_count_label = tk.Label(self, text="作业数量")
        self.job_count_label.grid(row=0, column=0)
        self.job_count_entry = tk.Entry(self)
        self.job_count_entry.grid(row=0, column=1)
        self.machine_count_label = tk.Label(self, text="机器数量")
        self.machine_count_label.grid(row=1, column=0)
        self.machine_count_entry = tk.Entry(self)
        self.machine_count_entry.grid(row=1, column=1)
        self.processing_times_label = tk.Label(self, text="加工时间矩阵")
        self.processing_times_label.grid(row=2, column=0)
        self.processing_times_text = tk.Text(self, height=10, width=30)
        self.processing_times_text.grid(row=2, column=1)
        self.run_button = tk.Button(self, text="运行", command=self.run)
        self.run_button.grid(row=3, column=0)
        self.result_label = tk.Label(self, text="结果")
        self.result_label.grid(row=3, column=1)
        self.quit_button = tk.Button(self, text="退出", command=self.master.destroy)
        self.quit_button.grid(row=4, column=1)

    def run(self):
        # 运行遗传算法求解流水车间调度问题
        job_count = int(self.job_count_entry.get())
        machine_count = int(self.machine_count_entry.get())
        processing_times = [list(map(int, row.split())) for row in self.processing_times_text.get("1.0", "end").split("\n") if row.strip()]
        best_chromosome = genetic_algorithm(50, job_count, machine_count, processing_times, 100)
        self.result_label.configure(text=str(best_chromosome))


# 创建主窗口
root = tk.Tk()

# 设置窗口标题
root.title("流水车间调度问题求解")

# 创建应用程序
app = Application(master=root)

# 进入消息循环
app.mainloop()

这个程序创建了一个 GUI 界面，包含作业数量、机器数量和加工时间矩阵三个输入框，以及一个运行按钮和一个结果标签。你可以输入作业数量、机器数量和加工时间矩阵，然后点击运行按钮，程序将使用遗传算法求解流水车间调度问题，并在结果标签中显示最优解的染色体。