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⛄ 内容介绍
有时间窗的车辆路径问题(Vehicle Routing Problem with Time Windows,VRPTW)因为其有重要的现实意义而备受关注.其时间窗即为客户接受服务的时间范围,该问题是运筹学和组合优化领域中的著名NP问题,是解决物流配送效率的关键,传统寻优方法效率低,耗时长,找不到满意解,往往导致物流成本过高.为了提高寻优效率,降低物流运送成本,基本遗传算法求解VRPTW问题.首先建立数学模型,然后基于大规模邻域搜索算法(LNS)生成遗传算法初始解,最后利用遗传算法在初始种群中找到最优解.计算结果表明,遗传算法可以更好求解车辆路径问题,有效降低物流成本.
⛄ 部分代码
%
function relatedness_ij = Relatedness(i, j, dist, vehicles_customer)
% n = size(dist, 1) - 1; % number of customer
NV = size(vehicles_customer, 1); % number of vehicles
d = dist(i + 1, j + 1);
[md, ~] = max((dist(i + 1, 2 : end)));
c = d / md; % distannce / max_distance
V = 1; % not in the same route
for k = 1 : NV
route = vehicles_customer{k}; % all customers in Kth route
findi = find(route == i, 1, 'first'); % return the position of customer if he is on this route
findj = find(route == j, 1, 'first'); %
% if i and j are on the same route, v = 0
if ~isempty(findi) && ~isempty(findj)
V = 0;
end
end
% relatedness is relative to the distance between 2 points and whether
% they are on the same route
relatedness_ij = 1 / (c + V);
end
⛄ 运行结果
⛄ 参考文献
[1]张露. (2020). 基于改进遗传算法求解带时间窗车辆路径规划问题. 中国物流与采购(14).
