基于分解协调法解决多库房库存-路径问题

Solving Multi-depot Inventory-routing Problems Based on Decomposition and Coordination Method

建立一个组合经常性库存费用、安全库存费用和随机路径费用模型后,首先,设计一种基于Monte-Carlo抽样求解路径期望费用方法。其次,针对解决此问题,使用传统分解协调法(DCM)的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。此外,对解决子系统随机需求车辆路径问题,为提高交叉熵法的性能,根据分位值改变大小,对用于更新Markov转移矩阵的路径,设计了自适应调整方法。仿真结果验证了该算法的有效性。

A model is established to incorporates working inventory, safety stock inventory and stochastic routing costs. Firstly, an effective algorithm is designed to find out the route expected cost by Monte-Carlo sampling,Secondly, poor convergence is solved by utilizing traditional decomposition and coordination method （DCM）, the coordination values are designed by genetic algorithm （GA） . Moreover, for dealing with the vehicle routing problem of stochastic demands for each subsystem, in order to improve the cross-entropy method＇ s performance, an adaptive adjustment scheme is developed for the routes used to update Markov transition matrix in terms of the improvement level of quintiles. Finally, simulation results prove the validity of the proposed method.