基于多候选储位的存取路径优化问题研究

针对单储位储存方式可能导致仓库存取通道拥挤和作业效率低的情形,提出了一种基于多候选储位的存取路径优化方法。首先分配了货物的存取储位,然后建立了多候选储位的车辆路径问题(MLVRP)模型,并基于储位优先解码原则设计了遗传算法,最后通过算例证明该方法的有效性和算法的高效性。多候选储位的方法可以为取货任务至少节约18.4%(两个候选储位)和21.8%(三个候选储位)的路程,算法迭代10000次只需要434s。

Optimization for Storage or Retrieval Routing Problem Based on Mutil-candidates Storages Location

With respect to the fact that every type of goods has only one storage location in warehouse will lead to crowded aisles and poor operational efficiency, this paper proposes an optimization approach for store and retrieval routing problem when multi-candidate storages locations are assigned to each type of goods. First,the storage locations are allocated to goods. Then, a model is built for the vehicle routing problem with multi-candidate storage locations for each type of goods. A genetic algorithm based on priority-based decoding scheme is developed to solve the model. Finally, a case is given to illustrate the effectiveness of the proposed method and the efficiency of the algorithm. The solution that two-candidate and three candidate storage locations are allocated to each type of goods could at least save 18.4% and 21.8% distance for retrievals respectively. The algorithm iterated for 10000 times costs 434 seconds.