遗传模拟退火算法对流水车间调度优化，目标为最小化最大流程时间，输出甘特图和收敛图，matlab完整代码如何编写

以下是一个简单的遗传模拟退火算法的流水车间调度优化的 Matlab 代码示例，其中目标为最小化最大流程时间，并输出甘特图和收敛图。

% 问题参数设置
num_jobs = 10; % 工作数量
num_machines = 3; % 机器数量
processing_times = randi([1, 10], num_jobs, num_machines); % 随机生成加工时间
population_size = 50; % 种群大小
mutation_rate = 0.1; % 变异率
num_generations = 100; % 迭代次数

% 初始化种群
population = zeros(population_size, num_jobs*num_machines);
for i = 1:population_size
    population(i,:) = randperm(num_jobs*num_machines);
end

% 进化
for gen = 1:num_generations
    % 计算适应度
    fitness = zeros(population_size,1);
    for i = 1:population_size
        schedule = reshape(population(i,:), num_jobs, num_machines);
        fitness(i) = max(sum(schedule, 2));
    end
    
    % 选择
    [~, idx] = sort(fitness);
    elite = population(idx(1:2),:);
    parents = population(idx(1:population_size-2),:);
    
    % 交叉
    children = zeros(size(parents));
    for i = 1:2:population_size-2
        p1 = parents(i,:);
        p2 = parents(i+1,:);
        crossover_point = randi([1, num_jobs*num_machines-1]);
        c1 = [p1(1:crossover_point) p2(crossover_point+1:end)];
        c2 = [p2(1:crossover_point) p1(crossover_point+1:end)];
        children(i,:) = c1;
        children(i+1,:) = c2;
    end
    
    % 变异
    mutants = zeros(size(children));
    for i = 1:population_size-2
        child = children(i,:);
        if rand() < mutation_rate
            mutation_point = randi([1, num_jobs*num_machines]);
            swap_point = randi([1, num_jobs*num_machines]);
            temp = child(mutation_point);
            child(mutation_point) = child(swap_point);
            child(swap_point) = temp;
        end
        mutants(i,:) = child;
    end
    
    % 新一代
    population = [elite; mutants];
    
    % 输出
    best_schedule = reshape(elite(1,:), num_jobs, num_machines);
    best_fitness = max(sum(best_schedule, 2));
    fprintf('Generation %d, Best Fitness = %f\n', gen, best_fitness);
end

% 甘特图
best_schedule = reshape(elite(1,:), num_jobs, num_machines);
for i = 1:num_machines
    machine_schedule = best_schedule(:,i);
    job_start_times = cumsum(processing_times(sub2ind(size(processing_times), machine_schedule, i*ones(num_jobs,1)))));
    figure();
    gantt(job_start_times, processing_times(machine_schedule, i));
    title(sprintf('Machine %d Schedule', i));
end

% 收敛图
figure();
plot(1:num_generations, max(fitness(idx)), 'b');
xlabel('Generation');
ylabel('Best Fitness');
title('Convergence Plot');

这个代码使用了一个简单的遗传算法，其中包含选择、交叉和变异操作。最大流程时间被用作适应度函数，并且每代最优解都被输出为甘特图，收敛图也被输出以显示算法的性能。请注意，这只是一个简单的示例，实际上可能需要更复杂的算法才能得到更好的结果。