Optimizing order picker problem using dynamic programming method

Document Type : Research Paper


1 Department of Mathematics, Faculty of Computer \& Mathematical Sciences, University Teknologi MARA, Cawangan Negeri Sembilan, Kampus Seremban, 70300 Seremban

2 Malaysia Institute of Transport (MITRANS), University Teknologi MARA, 40450 Shah Alam, Malaysia

3 Institute of Mathematical Sciences, University of Malaya, 50603 Kuala Lumpur, Malaysia


The order picking problem is one of the key elements in warehouse management.  The challenge increases during the new norm when orders can be made by going to the shop and also via online that results in high uncertainty in order volume. Despite that, customer expectation remains on fast delivery which requires the selling organizations to be able to provide fast and efficient service to meet the demand from customers.  In achieving this, among the contributing factors is efficient warehouse management especially in order picking, storage assignment, sufficient resource allocation, adequate manpower handling and proper tasking allocation. Thus, in this paper, a model for order picking is modified by considering the limited picking capacity of the Order Pickers (OP), the S-shaped route in the warehouse plan and the need for complete order (all demanded items are picked). The modified model is adapted as a Dynamic Programming problem with the objective of minimizing the time taken (through minimizing distance travelled) in picking each order. The results show that testing with a set of secondary data, the modified model shows a reduction of 24.19\% in travel distance compared to using Shortest Path Problem (SPP) and Traveling Salesman Problem (TSP). At the same time, the application of the modified model using the real data shows a reduction of 11.6\% in the travelled distance as well as more quality task allocation among the OPs.


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Volume 13, Issue 1
March 2022
Pages 1985-1998
  • Receive Date: 03 October 2021
  • Revise Date: 13 November 2021
  • Accept Date: 21 November 2021