Minimizing order picking makespan with multiple pickers in a wave picking warehouse
Manjeet Singh and
Omid Sanei Bajgiran
International Journal of Production Economics, 2018, vol. 206, issue C, 169-183
In this paper, order assignment, order batching and picker routing problems with multiple pickers in a wave picking warehouse of a major US third party logistics company is studied and modeled mathematically. The proposed mathematical model is solved using an exact algorithm. Since the exact algorithm suffers from long CPU time, a Lagrangian decomposition heuristic combined with particle swarm optimization (LD-PSO) algorithm is proposed, which performs well for small size waves. To solve large-scale problems, a hybrid parallel simulated annealing and ant colony optimization (PSA-ACO) is presented. The proposed methods are tested using the warehouse data. The results are compared against the minimum makespan impact (MMI) heuristic that is currently being used in the warehouse and a state-of-the-art variable neighborhood descent (VND). While PSA-ACO slightly outperforms VND, for picking large waves, PSA-ACO and VND can improve the makespan by approximately 7.8% and 6.9% over MMI respectively. Numerical experiments show that increasing number of pickers and picking capacity has a greater impact on reducing makespan when the ratio of orders to number of pickers or picking capacity is high.
Keywords: Order picking; Order batching; Routing optimization; Order assignment; Lagrangian decomposition; Metaheuristics (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:206:y:2018:i:c:p:169-183
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