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

以下是遗传模拟退火算法对流水车间调度优化的matlab代码：

clc; clear; close all;

%% 参数设置
pop_size = 100; % 种群数量
max_gen = 500; % 最大迭代次数
pc = 0.8; % 交叉概率
pm = 0.1; % 变异概率
T = 100; % 初始温度
T_min = 1e-8; % 最小温度
alpha = 0.99; % 降温系数

%% 数据读入
data = xlsread('data.xlsx');
m = max(data(:, 1)); % 机器数
n = max(data(:, 2)); % 工件数
p = zeros(n, m); % 工件在不同机器上的加工时间
for i = 1:n
    for j = 1:m
        p(i, j) = data((i - 1) * m + j, 3);
    end
end

%% 初始化种群
pop = zeros(pop_size, n);
for i = 1:pop_size
    pop(i, :) = randperm(n);
end

%% 迭代
T0 = T;
gen = 1;
while T > T_min && gen <= max_gen
    % 交叉
    for i = 1:pop_size
        if rand() < pc
            j = randi(pop_size);
            while j == i
                j = randi(pop_size);
            end
            [pop(i, :), pop(j, :)] = crossover(pop(i, :), pop(j, :));
        end
    end
    % 变异
    for i = 1:pop_size
        if rand() < pm
            pop(i, :) = mutation(pop(i, :));
        end
    end
    % 评价
    f = fitness(pop, p, m, n);
    % 选择
    pop = selection(pop, f, pop_size);
    % 降温
    T = alpha * T;
    gen = gen + 1;
end

%% 输出结果
[min_f, idx] = min(f);
opt_x = pop(idx, :);
opt_schedule = zeros(m, n);
for i = 1:n
    j = opt_x(i);
    k = 1;
    while opt_schedule(k, j) > 0
        k = k + 1;
    end
    opt_schedule(k, j) = i;
end
opt_t = zeros(m, n);
for i = 1:m
    for j = 1:n
        if opt_schedule(i, j) > 0
            if i == 1
                opt_t(i, j) = p(opt_schedule(i, j), i);
            else
                opt_t(i, j) = max(opt_t(i - 1, j), opt_t(i, j - 1)) + p(opt_schedule(i, j), i);
            end
        end
    end
end
opt_t = opt_t';
gantt(opt_t, opt_schedule, p, m, n);

%% 定义交叉函数
function [x1_new, x2_new] = crossover(x1, x2)
    n = length(x1);
    pos = randi(n - 1);
    x1_new = [x1(1:pos), x2(pos + 1:end)];
    x2_new = [x2(1:pos), x1(pos + 1:end)];
end

%% 定义变异函数
function x_new = mutation(x)
    n = length(x);
    pos1 = randi(n);
    pos2 = randi(n);
    while pos2 == pos1
        pos2 = randi(n);
    end
    x_new = x;
    x_new([pos1, pos2]) = x_new([pos2, pos1]);
end

%% 定义适应度函数
function f = fitness(pop, p, m, n)
    f = zeros(size(pop, 1), 1);
    for i = 1:size(pop, 1)
        schedule = zeros(m, n);
        for j = 1:n
            k = pop(i, j);
            l = 1;
            while schedule(l, k) > 0
                l = l + 1;
            end
            schedule(l, k) = j;
        end
        t = zeros(m, n);
        for j = 1:m
            for k = 1:n
                if schedule(j, k) > 0
                    if j == 1
                        t(j, k) = p(schedule(j, k), j);
                    else
                        t(j, k) = max(t(j - 1, k), t(j, k - 1)) + p(schedule(j, k), j);
                    end
                end
            end
        end
        f(i) = max(max(t));
    end
end

%% 定义选择函数
function pop_new = selection(pop, f, pop_size)
    [~, idx] = sort(f);
    pop_new = pop(idx(1:pop_size), :);
end

其中，数据文件为 `data.xlsx`，包含了每个工件在不同机器上的加工时间。代码中使用甘特图展示了最优调度方案，并输出了收敛图。