使用Matlab来求解应急物流中心选址问题
已经有完整的代码,但是需要在此基础上改一点约束条件,应该是很简单的只不过本人不太懂代码操作
希望可以联系,提供所有数据和完整代码,有偿

遗传算法求解应急物流中心选址问题
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5条回答 默认 最新
- CodeBytes 2023-02-27 11:07关注
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clc clear close all % 常量定义 MAX_gen = 4000; % 最大迭代步数 center.Station = [31708.622,7772.865;31705.726,7766.996;31730.340,7771.543;31709.847,7799.784;31702.607,7775.014;31700.937,7774.105]; center.chubei = [5000, 5000, 5000, 5000, 5000, 5000]; cost.construction = 2500 * 10000; % 元 cost.storage = 100; P = 0.1; % 发生灾害的概率 % 车辆和无人机的运输速度和单位成本 vehicle_speed = 60; % km/h vehicle_cost = 5; % 元/每单位资源/km drone_speed = 72; % km/h drone_cost = 7; % 元/每单位资源/km % 需求点及其需求量 requirement.Station = [31699.8,7774.1; 31739.9,7767.4; 31702.0,7775.7; 31749.9,7777.4; 31733.2,7789.7; 31688.6,7802.1; 31710.9,7800.5; 31688.6,7823.6; 31767.7,7812.8; 31667.5,7769.1]; requirement.need = [670, 800, 800, 950, 3010, 800, 2560, 1320, 1060, 950]; % 计算车辆和无人机运输距离 vehicle_indices = 1:6; drone_indices = 7:10; vehicle_distances = pdist2(center.Station(vehicle_indices, :), requirement.Station); drone_distances = pdist2(center.Station(drone_indices, :), requirement.Station); % 初始化种群 pop_length = size(requirement.Station, 1); pop_num = 100; pop_choose = size(center.Station, 1); Pcc = 0.8; % 交叉概率 Pmm = 0.05; % 变异概率 popus = init(pop_length, pop_num, pop_choose); % 迭代优化 for gen = 1:MAX_gen sprintf('迭代次数%d', gen) for i = 1:size(popus, 1) % 计算种群个体的适应度 f = fit(popus, center, requirement, cost, P); % 更新最优解 if min(f) <= best.min_f advance_k = advance_k + 1; ff_better{advance_k} = f; f_better(advance_k) = min(f); best.min_f = min(f); [re, index] = min(f); best.jieguo = popus{index(1)}; best.popus = popus; else advance_k = advance_k + 1; ff_better{advance_k} = ff_better{advance_k-1}; f_better(advance_k) = f_better(advance_k-1); popus = best.popus; end PPP{gen} = popus; popus = choose(popus, f, Pcc, Pmm, center); % 将种群中每个个体分配到车辆或无人机 vehicle_assignments = zeros(size(popus)); drone_assignments = zeros(size(popus)); for j = 1:size(popus, 1) vehicle_indices = find(popus{j} <= 6); drone_indices = find(popus{j} > 6); vehicle_distance = sum(vehicle_distances(vehicle_indices, j)); drone_distance = sum(drone_distances(drone_indices, j)); vehicle_time = vehicle_distance / vehicle_speed; drone_time = drone_distance / drone_speed; vehicle_cost_total = sum(vehicle_cost * requirement.need(vehicle_indices) * vehicle_distances(vehicle_indices, j)); drone_cost_total = sum(drone_cost * requirement.need(drone_indices) * drone_distances(drone_indices, j)); if vehicle_time + drone_time <= 24 && vehicle_cost_total + drone_cost_total <= cost.construction vehicle_assignments(j, vehicle_indices) = 1; drone_assignments(j, drone_indices) = 1; end end % 输出方案 for j = 1:size(vehicle_assignments, 1) vehicle_indices = find(vehicle_assignments(j, :)); drone_indices = find(drone_assignments(j, :)); if ~isempty(vehicle_indices) && ~isempty(drone_indices) fprintf('方案 %d: 候选点 %d 送达需求点 %s(车辆运输)和 %s(无人机运输)\n', j, i, num2str(vehicle_indices), num2str(drone_indices)); elseif ~isempty(vehicle_indices) fprintf('方案 %d: 候选点 %d 送达需求点 %s(车辆运输)\n', j, i, num2str(vehicle_indices)); elseif ~isempty(drone_indices) fprintf('方案 %d: 候选点 %d 送达需求点 %s(无人机运输)\n', j, i, num2str(drone_indices)); else fprintf('方案 %d: 候选点 %d 未选取任何需求点\n', j, i); end end end end
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