请生成该模型的matlab代码

生成基于免疫遗传算法的医疗器械物流园区选址模型的MATLAB代码涉及多个步骤，包括初始化参数、定义适应度函数、编码和解码个体、交叉和变异操作、免疫算子等。以下是一个简化版本的示例代码：

% 参数设置
num_supply_points = 10;
num_candidate_parks = 5;
num_customers = 13;
population_size = 30;
max_generations = 200;
cross_prob = 0.8;
mutate_prob = 0.1;

% 初始化数据
supply_to_park_distances = [30.5, 70.0, 51.0, 72.0, 92.0, 53.2, 71.6, 82.0, 43.0, 61.0; ...
                            83.3, 74.0, 83.0, 84.0, 29.0, 64.0, 36.0, 76.0, 30.0, 85.0; ...
                            53.0, 79.0, 71.0, 60.0, 67.0, 27.2, 79.8, 34.0, 46.0, 79.5; ...
                            66.0, 30.8, 42.0, 53.6, 90.0, 39.0, 27.0, 37.3, 38.0, 92.2; ...
                            37.0, 69.0, 36.6, 79.2, 38.3, 41.3, 41.0, 31.0, 70.4, 84.4];
park_to_customer_distances = [5.5, 20.2, 11.0, 5.7, 13.2, 4.4, 5.2, 9.0, 3.3, 18.0, 18.2, 24.0, 24.6; ...
                              4.5, 10.0, 5.6, 14.8, 10.3, 13.3, 16.0, 6.6, 16.4, 17.2, 21.0, 19.2, 19.5; ...
                              14.0, 19.6, 7.0, 13.0, 18.1, 16.1, 13.4, 20.4, 5.0, 3.5, 25.0, 19.3, 18.5; ...
                              13.8, 3.0, 14.3, 6.0, 14.5, 12.0, 13.1, 17.8, 4.0, 17.4, 22.0, 20.0, 20.3; ...
                              15.0, 12.8, 12.2, 13.6, 3.8, 15.2, 8.0, 4.2, 9.3, 15.3, 19.7, 18.3, 19.0];
supply_quantities = [730, 770, 844, 909, 584, 594, 619, 708, 613, 799];
customer_demands = [368, 152, 529, 255, 419, 416, 455, 480, 147, 120, 303, 357, 282];
construction_costs = [10, 12.8, 30.39, 11, 15] * 1e8;
variable_costs = [350, 400, 280, 300, 250];
transportation_rate = 0.75;

% 权重设置
alpha = 0.3;
beta = 0.3;
gamma = 0.4;

% 初始化种群
population = randi([0, 1], population_size, num_candidate_parks);

% 主循环
for generation = 1:max_generations
    % 计算适应度
    fitness_values = zeros(population_size, 1);
    for i = 1:population_size
        chromosome = population(i, :);
        fitness_values(i) = calculate_fitness(chromosome, supply_to_park_distances, park_to_customer_distances, ...
                                              supply_quantities, customer_demands, construction_costs, variable_costs, transportation_rate, alpha, beta, gamma);
    end
    
    % 选择操作
    selected_population = select_population(population, fitness_values, population_size);
    
    % 交叉操作
    crossed_population = crossover(selected_population, cross_prob);
    
    % 变异操作
    mutated_population = mutate(crossed_population, mutate_prob);
    
    % 免疫操作
    population = immune_operator(mutated_population, supply_to_park_distances, park_to_customer_distances, ...
                                 supply_quantities, customer_demands, construction_costs, variable_costs, transportation_rate, alpha, beta, gamma);
end

% 输出最优解
best_chromosome = population(find(fitness_values == max(fitness_values), 1));
best_fitness = max(fitness_values);
disp('最优选址方案:');
disp(best_chromosome);
disp(['目标函数值: ', num2str(best_fitness)]);

% 计算适应度函数
function fitness = calculate_fitness(chromosome, supply_to_park_distances, park_to_customer_distances, ...
                                     supply_quantities, customer_demands, construction_costs, variable_costs, transportation_rate, alpha, beta, gamma)
    fixed_cost = sum(chromosome .* construction_costs);
    variable_cost = sum(chromosome .* variable_costs);
    
    total_transport_cost = 0;
    for j = 1:num_candidate_parks
        if chromosome(j) == 1
            for i = 1:num_supply_points
                transport_cost_i_j = supply_quantities(i) * supply_to_park_distances(j, i) * transportation_rate;
                total_transport_cost = total_transport_cost + transport_cost_i_j;
            end
            for k = 1:num_customers
                transport_cost_j_k = customer_demands(k) * park_to_customer_distances(j, k) * transportation_rate;
                total_transport_cost = total_transport_cost + transport_cost_j_k;
            end
        end
    end
    
    max_distance = max(park_to_customer_distances(:, find(chromosome == 1)));
    
    fitness = alpha * (fixed_cost + variable_cost) + beta * total_transport_cost + gamma * max_distance;
end

% 选择操作
function selected_population = select_population(population, fitness_values, population_size)
    probabilities = fitness_values / sum(fitness_values);
    selected_indices = randsample(1:population_size, population_size, true, probabilities);
    selected_population = population(selected_indices, :);
end

% 交叉操作
function crossed_population = crossover(population, cross_prob)
    crossed_population = population;
    for i = 1:2:population_size-1
        if rand < cross_prob
            cross_point = randi([1, num_candidate_parks-1]);
            temp = crossed_population(i, cross_point+1:end);
            crossed_population(i, cross_point+1:end) = crossed_population(i+1, cross_point+1:end);
            crossed_population(i+1, cross_point+1:end) = temp;
        end
    end
end

% 变异操作
function mutated_population = mutate(population, mutate_prob)
    mutated_population = population;
    for i = 1:population_size
        for j = 1:num_candidate_parks
            if rand < mutate_prob
                mutated_population(i, j) = 1 - mutated_population(i, j);
            end
        end
    end
end

% 免疫操作
function population = immune_operator(population, supply_to_park_distances, park_to_customer_distances, ...
                                      supply_quantities, customer_demands, construction_costs, variable_costs, transportation_rate, alpha, beta, gamma)
    for i = 1:population_size
        chromosome = population(i, :);
        fitness = calculate_fitness(chromosome, supply_to_park_distances, park_to_customer_distances, ...
                                    supply_quantities, customer_demands, construction_costs, variable_costs, transportation_rate, alpha, beta, gamma);
        
        % 注射疫苗
        vaccine = generate_vaccine(supply_to_park_distances, park_to_customer_distances, supply_quantities, customer_demands, construction_costs, variable_costs, transportation_rate, alpha, beta, gamma);
        vaccinated_chromosome = chromosome + vaccine;
        vaccinated_chromosome(vaccinated_chromosome > 1) = 1;
        vaccinated_chromosome(vaccinated_chromosome < 0) = 0;
        
        vaccinated_fitness = calculate_fitness(vaccinated_chromosome, supply_to_park_distances, park_to_customer_distances, ...
                                               supply_quantities, customer_demands, construction_costs, variable_costs, transportation_rate, alpha, beta, gamma);
        
        if vaccinated_fitness < fitness
            population(i, :) = vaccinated_chromosome;
        end
    end
end

% 生成疫苗
function vaccine = generate_vaccine(supply_to_park_distances, park_to_customer_distances, supply_quantities, customer_demands, construction_costs, variable_costs, transportation_rate, alpha, beta, gamma)
    num_parks = size(supply_to_park_distances, 1);
    vaccine = zeros(1, num_parks);
    for j = 1:num_parks
        if rand < 0.1
            vaccine(j) = 1;
        end
    end
end

### 说明
1. **参数设置**：定义了问题的基本参数，如供应点、候选园区、客户点的数量，种群大小，最大迭代次数等。
2. **初始化数据**：加载了各个点之间的距离、供应量、需求量、建设成本、可变成本等数据。
3. **主循环**：执行遗传算法的主要迭代过程，包括适应度计算、选择、交叉、变异和免疫操作。
4. **适应度函数**：计算每个个体的适应度值，考虑了固定成本、可变成本、运输成本和客户满意度。
5. **选择操作**：根据适应度值选择个体。
6. **交叉操作**：通过单点交叉生成新的个体。
7. **变异操作**：通过随机翻转某些基因位来引入变异。
8. **免疫操作**：通过注射疫苗来增强个体的适应性。

这个代码是一个基本的实现，可以根据实际需求进行调整和优化。