Optimizing Order Batching in Order Picking Systems: Hybrid Grouping Genetic Algorithm

Abstract

Optimizing order batching is an essential decision problem for efficient manual order picking systems in distribution warehouses. It involves traversing through a distribution warehouse to collect items so as to satisfy customer orders. For efficient operation of manual order picking systems, order batching should be optimized. Customer orders should be grouped into picking orders of limited sizes, while ensuring that the total distance traversed by order pickers is minimized. To solve the problem, a hybrid grouping genetic algorithm (HGGA), incorporating unique grouping operators, constructive heuristics, and other heuristic algorithms, is proposed. Based on benchmark heuristics, extensive numerical experiments are conducted to test the utility of the algorithm. Comparative computational results demonstrate that the HGGA can provide high-quality solutions. Additionally, the computation times of the HGGA are generally shorter when compared to other algorithms. Thus, reduced length of picker tours leads to the overall reduction of the order picking time, which cuts down on overtime, workforce size, and the overall operational costs, while improving the quality of service.