一种解决多库房随机车辆路径问题方法

基于分解协调技术(DCT),根据启发式法,将用户分解为耦合和非耦合用户。针对解决此问题,使用传统协调参数收敛性差的问题,应用遗传算法(GA)设计了协调参数。对解决子系统随机需求车辆路径问题,为提高交叉熵法性能,对用于更新Markov转移矩阵的路径,设计了根据分位值改变大小的自适应调整方法。仿真结果验证了该算法的有效性。

Method for Solving Multi-depot Stochastic Vehicle Routing Problems

Based on decomposition and coordination technology(DCT),the customers were firstly divided into coupling and non-coupling customers by a heuristic algorithm.Secondly,due to the bad convergence while using traditional DCT to solve the problems,the coordination values were designed by using genetic algorithm(GA).While dealing with the vehicle routing problems with stochastic demands for each subsystem,in order to improve the cross-entropy algorithm's performance,an adaptive adjustment scheme was developed for the routes used to update Markov transition matrix in terms of the improvement level of quintiles.Simulation results prove the validity of the proposed method.