Optimizing order batching in order picking systems: Hybrid grouping genetic algorithm

Optimized order batching is very important for efficient operation of manual order picking systems in distribution warehouses. The chapter presented a hybrid grouping genetic algorithm (HGGA) for the order batching problem. Extensive numerical experiments were used to test the utility of the algorithm. Comparative performance analysis of the algorithm and other benchmark heuristics in the literature showed that HGGA can provide better solutions. In terms of computation times (CPU times), the HGGA computation times were generally shorter when compared to other algorithms. The proposed HGGA can reduce the length of picker tours significantly. In practice, this demonstrates an effective reduction of the overall picking time, which may translate to cutting down of operational costs and reduction of overtime or workforce. Improved solution quality and computation times also imply that the average lead time for customer orders is also reduced, which ultimately leads to high quality of service. In the long run, this will have a positive impact on the survival of the order picking system and the overall distribution warehouse system. It will be interesting to carry out further studies the impact of order batching systems on related activities such as article location, picker routing, and warehouse design. It is hoped that such integrated perspectives will greatly improve the overall performance of logistics and warehouse systems.