请用matlab代码解决算例

要使用MATLAB代码解决您提供的算例，我们需要编写一个遗传算法来优化B2C环境下生鲜农产品的物流配送路径。下面是一个简化版的MATLAB代码示例，用于解决这个问题：

function main
    % 参数设置
    popSize = 60;        % 种群大小
    maxGen = 300;        % 最大迭代次数
    Pc = 0.5;            % 交叉概率
    Pm = 0.06;           % 变异概率
    Q = 100;             % 配送车最大载重量 (kg)
    deliveryCost = 1;    % 单位距离配送成本 (元/km)
    productPrice = 20;   % 生鲜农产品单价 (元/kg)
    T = 48;              % 生鲜农产品保质期 (小时)
    w1 = 30;             % 早于期望收货时间的惩罚费用 (元/小时)
    w2 = 40;             % 晚于期望收货时间的惩罚费用 (元/小时)

    % 初始化配送中心和顾客信息
    centers = [6, 15; 14, 12; 4, 8]; % 配送中心坐标
    customers = struct();
    customers.A = readCustomers('A');
    customers.B = readCustomers('B');
    customers.C = readCustomers('C');

    % 初始化种群
    population = initPopulation(customers, popSize);

    % 遗传算法主循环
    for gen = 1:maxGen
        % 计算适应度
        fitness = calculateFitness(population, customers, centers, Q, deliveryCost, productPrice, T, w1, w2);
        
        % 选择操作
        selectedPop = selection(population, fitness);
        
        % 交叉操作
        crossedPop = crossover(selectedPop, Pc);
        
        % 变异操作
        mutatedPop = mutation(crossedPop, Pm);
        
        % 更新种群
        population = mutatedPop;
    end

    % 获取最佳个体
    bestChromosome = population{find(fitness == max(fitness), 1)};
    
    % 输出结果
    displayResults(bestChromosome, customers, centers, Q, deliveryCost, productPrice, T, w1, w2);
end

function cust = readCustomers(center)
    % 读取顾客信息
    if strcmp(center, 'A')
        cust = [
            1, 10, 1, 10, 10, 12, 15, 18; % x, y, demand, ET', LT', ET, LT
            2, 12, 15, 1, 10, 10, 12, 15;
            3, 8, 6, 1, 10, 10, 12, 15;
            4, 15, 18, 1, 10, 10, 12, 15;
            5, 18, 15, 1, 10, 10, 12, 15;
            6, 12, 8, 1, 10, 10, 12, 15;
            7, 6, 12, 1, 10, 10, 12, 15;
            8, 15, 6, 1, 10, 10, 12, 15;
            9, 18, 12, 1, 10, 10, 12, 15
        ];
    elseif strcmp(center, 'B')
        cust = [
            1, 10, 10, 1, 10, 10, 12, 15;
            2, 12, 15, 1, 10, 10, 12, 15;
            3, 8, 6, 1, 10, 10, 12, 15;
            4, 15, 18, 1, 10, 10, 12, 15;
            5, 18, 15, 1, 10, 10, 12, 15;
            6, 12, 8, 1, 10, 10, 12, 15;
            7, 6, 12, 1, 10, 10, 12, 15;
            8, 15, 6, 1, 10, 10, 12, 15
        ];
    else
        cust = [
            1, 10, 10, 1, 10, 10, 12, 15;
            2, 12, 15, 1, 10, 10, 12, 15;
            3, 8, 6, 1, 10, 10, 12, 15;
            4, 15, 18, 1, 10, 10, 12, 15;
            5, 18, 15, 1, 10, 10, 12, 15;
            6, 12, 8, 1, 10, 10, 12, 15;
            7, 6, 12, 1, 10, 10, 12, 15;
            8, 15, 6, 1, 10, 10, 12, 15;
            9, 18, 12, 1, 10, 10, 12, 15
        ];
    end
end

function population = initPopulation(customers, popSize)
    % 初始化种群
    population = cell(1, popSize);
    for i = 1:popSize
        chromosome = [];
        for center = fieldnames(customers)'
            custList = randperm(size(customers.(center{:}), 1));
            chromosome = [chromosome, custList];
        end
        population{i} = chromosome;
    end
end

function fitness = calculateFitness(population, customers, centers, Q, deliveryCost, productPrice, T, w1, w2)
    % 计算适应度
    fitness = zeros(1, length(population));
    for i = 1:length(population)
        chromosome = population{i};
        totalCost = 0;
        customerIndex = 1;
        for center = fieldnames(customers)'
            custList = chromosome(customerIndex:customerIndex + size(customers.(center{:}), 1) - 1);
            routes = createRoutes(custList, customers.(center{:}), centers(center), Q);
            for j = 1:length(routes)
                route = [centers(center); routes{j}];
                cost = 0;
                time = 0;
                load = 0;
                for k = 1:length(route)-1
                    dist = norm(route(k, :) - route(k+1, :));
                    time = time + dist / 15 + 10; % 速度15km/h，停留10分钟
                    load = load + customers.(center{:})(route(k+1, 1), 3);
                    if time < customers.(center{:})(route(k+1, 1), 4)
                        cost = cost + w1 * (customers.(center{:})(route(k+1, 1), 4) - time);
                    elseif time > customers.(center{:})(route(k+1, 1), 5)
                        cost = cost + w2 * (time - customers.(center{:})(route(k+1, 1), 5));
                    end
                    freshnessLoss = productPrice * customers.(center{:})(route(k+1, 1), 3) * (1 - exp(-time/T));
                    cost = cost + dist * deliveryCost + freshnessLoss;
                end
                totalCost = totalCost + cost;
            end
            customerIndex = customerIndex + size(customers.(center{:}), 1);
        end
        fitness(i) = 1 / totalCost; % 适应度定义为总成本的倒数
    end
end

function selectedPop = selection(population, fitness)
    % 选择操作
    selectedPop = {};
    totalFitness = sum(fitness);
    probabilities = fitness / totalFitness;
    for i = 1:length(population)
        idx = find(rand <= cumsum(probabilities), 1);
        selectedPop{end+1} = population{idx};
    end
end

function crossedPop = crossover(selectedPop, Pc)
    % 交叉操作
    crossedPop = selectedPop;
    for i = 1:2:length(selectedPop)-1
        if rand < Pc
            p1 = selectedPop{i};
            p2 = selectedPop{i+1};
            crossPoint1 = randi([1, length(p1)-1]);
            crossPoint2 = randi([crossPoint1+1, length(p1)]);
            child1 = [p1(1:crossPoint1), p2(crossPoint1+1:crossPoint2), p1(crossPoint2+1:end)];
            child2 = [p2(1:crossPoint1), p1(crossPoint1+1:crossPoint2), p2(crossPoint2+1:end)];
            crossedPop{i} = unique(child1);
            crossedPop{i+1} = unique(child2);
        end
    end
end

function mutatedPop = mutation(crossedPop, Pm)
    % 变异操作
    mutatedPop = crossedPop;
    for i = 1:length(crossedPop)
        if rand < Pm
            mutPoint = randi(length(crossedPop{i}));
            newGene = randi(length(crossedPop{i}));
            while newGene == crossedPop{i}(mutPoint)
                newGene = randi(length(crossedPop{i}));
            end
            temp = crossedPop{i}(mutPoint);
            crossedPop{i}(mutPoint) = crossedPop{i}(newGene);
            crossedPop{i}(newGene) = temp;
        end
    end
end

function routes = createRoutes(custList, customers, center, Q)
    % 创建配送路线
    routes = {};
    currentRoute = [];
    currentLoad = 0;
    for i = 1:length(custList)
        if currentLoad + customers(custList(i), 3) <= Q
            currentRoute = [currentRoute, custList(i)];
            currentLoad = currentLoad + customers(custList(i), 3);
        else
            routes{end+1} = currentRoute;
            currentRoute = custList(i);
            currentLoad = customers(custList(i), 3);
        end
    end
    if ~isempty(currentRoute)
        routes{end+1} = currentRoute;
    end
end

function displayResults(chromosome, customers, centers, Q, deliveryCost, productPrice, T, w1, w2)
    % 显示结果
    fprintf('Optimal Delivery Routes:\n');
    customerIndex = 1;
    for center = fieldnames(customers)'
        custList = chromosome(customerIndex:customerIndex + size(customers.(center{:}), 1) - 1);
        routes = createRoutes(custList, customers.(center{:}), centers(center), Q);
        fprintf('Center %s:\n', center);
        for j = 1:length(routes)
            route = [centers(center); routes{j}];
            fprintf('  Route %d: ', j);
            for k = 1:length(route)
                if k == 1
                    fprintf('%s -> ', num2str(center));
                else
                    fprintf('%d -> ', route(k));
                end
            end
            fprintf('%s\n', num2str(center));
        end
        customerIndex = customerIndex + size(customers.(center{:}), 1);
    end
    totalCost = 0;
    for center = fieldnames(customers)'
        custList = chromosome(customerIndex:customerIndex + size(customers.(center{:}), 1) - 1);
        routes = createRoutes(custList, customers.(center{:}), centers(center), Q);
        for j = 1:length(routes)
            route = [centers(center); routes{j}];
            cost = 0;
            time = 0;
            load = 0;
            for k = 1:length(route)-1
                dist = norm(route(k, :) - route(k+1, :));
                time = time + dist / 15 + 10;
                load = load + customers.(center{:})(route(k+1, 1), 3);
                if time < customers.(center{:})(route(k+1, 1), 4)
                    cost = cost + w1 * (customers.(center{:})(route(k+1, 1), 4) - time);
                elseif time > customers.(center{:})(route(k+1, 1), 5)
                    cost = cost + w2 * (time - customers.(center{:})(route(k+1, 1), 5));
                end
                freshnessLoss = productPrice * customers.(center{:})(route(k+1, 1), 3) * (1 - exp(-time/T));
                cost = cost + dist * deliveryCost + freshnessLoss;
            end
            totalCost = totalCost + cost;
        end
        customerIndex = customerIndex + size(customers.(center{:}), 1);
    end
    fprintf('Total Cost: %.2f\n', totalCost);
end

### 说明
1. **参数设置**：设置了种群大小、最大迭代次数、交叉概率、变异概率等参数。
2. **初始化配送中心和顾客信息**：读取各配送中心及其负责的顾客信息。
3. **初始化种群**：随机生成初始种群。
4. **遗传算法主循环**：包括适应度计算、选择、交叉、变异等操作。
5. **输出结果**：显示最佳配送路径和总成本。

### 注意事项
- 顾客信息需要根据实际情况调整。
- 代码中的一些参数（如配送车速度、停留时间等）也需要根据实际情况调整。
- 代码中的一些细节（如交叉和变异操作）可以根据具体需求进一步优化。