摘要
针对货物存放点和货物体积的不确定性,提出应用交叉熵法解决拣货车路径问题的方法,由于目标函数的复杂性,设计一种基于Monte-Carlo抽样求解路径期望距离的有效方法。为了提高标准交叉熵(CE)法的性能,设计了随分位值大小发生变化的更新Markov转移矩阵关键路径的自适应调整算法。计算结果验证了采用该方法解决此问题的鲁棒性和有效性。
In view of the uncertainty of the depot locations and cargo volume, the paper proposes an approach to the picking vehicle routing problem （PVRP） that effectively incorporates the cross-entrophy method and a Monte-Carlo-based sampling. To enhance the performanc of the standard cross-entrophy method, an adaptive adjustment scheme is developed for the crucial routes used to update Markov transition matrix in terms of the improvement level of quintiles. The subsequent computational study verifies the robustness and effectiveness of the approach for such problems.
出处
《物流技术》
2010年第5期67-69,73,共4页
Logistics Technology
关键词
拣货车路径
交叉熵法
随机存放点
体积
picking vehicle routing
cross-entrophy
stochastic depot
volume
