Order Batching in A Picker-to-Part Warehousing System of A Supply Chain
Order batch policy determines the way orders are combined into batches so that the travel distance of orders fulfilled can be reduced. Since the order batching problem (OBP) is NP-hard in the strong sense, meta-heuristic approaches have been proposed and genetic algorithm (GA) is the most commonly applied method for picker-to-part warehousing systems. However, for the grouping problem, group genetic algorithm (GGA) is more suitable than the classic GA since the encodings of GA used are not adapted to the cost function to be optimized. This paper develops a meta-heuristic method based on GGA for OBP and defines an indicator by incorporating the similarity of the picking positions of orders into routing characteristics for each of the three routing policies of return, traversal and largest gap. The results of the numerical experiment indicate that the throughput of the proposed heuristic method is statistically better than the existing ones for a picker-to-part warehousing system.
Keywords - Supply Chain Management,Picker-to-Part Warehouse System, .Order Batching, Group Genetic Algorithm