Real-time order picking planning framework for warehouses and distribution centres

Abstract

This paper proposes a dynamic-order picking (DOP) framework where batches and picker routes are continuously modified in response to urgent orders arrival and disruption events. In the proposed framework, we consider conflicts between pickers in narrow aisles to ensure a smooth replanning function. For this purpose, we first formulate a static joint-order batching and sequencing model (J-OBS) that takes into account congestion time. Then, we propose a replanning model (D-J-OBS) that utilises real-time input from multiple information systems. As a solution approach and due to the NP-hardness of the problem, we use a tabu search algorithm to solve practical sizes of the static and the dynamic problems. The results indicate that the DOP framework generates significant tardiness savings compared to online-order picking, especially for systems with high degree of dynamism.

Keywords:

• Logistics
• dynamic-order picking
• joint-order batching and sequencing
• mixed-integer linear programming
• tabu search

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Husam Dauod

Husam Dauod, Ph.D. received his M.S. (2016) and Ph.D. (2019) in Industrial and Systems Engineering from Binghamton University, SUNY. He is currently a Research Scientist at Intel Corp. His research interests include mathematical modelling, optimisation, and simulation and their application in smart manufacturing, warehousing, and supply chain.

Daehan Won

Daehan Won, Ph.D. received a B.S. (2008) and M.S. (2010) in industrial engineering from Korea Advanced Institute of Science and Technology, Daejeon, S. Korea, and Ph.D. (2016) in industrial and systems engineering from University of Washington, Seattle, WA. In 2016, he joined the Department of Systems Science and Industrial Engineering, Binghamton University, SUNY as an assistant professor. His research interests lie in mathematical programming in large-scale programming and data science applications for various fields such as healthcare and manufacturing. Recently, he is working on designing new platforms for intelligent electronics manufacturing systems to cope with advances in industry 4.0. as well as Health Informatics in Biomedical Engineering.