Optimal Order Batching in Warehouse Management: A Data-driven Robust Approach

Vedat Bayram, Department of Industrial Engineering, TED University


More than ever, the role of warehouses is crucial to supply chain efficiency. Optimizing warehouse processes has direct impact on supply chain responsiveness, timely order fulfillment and customer satisfaction. In this work, we focus on the picking process in warehouse management and study it from a data perspective. Using historical data from an industrial partner, we introduce, model, and study the Robust Order Batching Problem (ROBP) which groups orders into batches to minimize total order processing time taking into account uncertainty caused by system congestion and human behavior. We provide a generalizable, data-driven approach that overcomes warehouse-specific assumptions characterizing the majority of work in the literature. We analyze historical data to understand the processes in the warehouse, to predict processing times, and to improve order processing. For that purpose, we introduce the ROBP and develop an efficient branch and price algorithm based on simultaneous column and row generation. The algorithm is embedded with alternative prediction models such as linear regression and random forest to predict processing time of a batch. We conduct extensive computational experiments to test the performance of the proposed approach and to derive managerial insights based on real data. We found that forming batches at full capacity is not always optimal, and that the congestion level and level of conservatism have a significant impact on batching decisions. The data-driven prescriptive analytics tool we propose achieves savings of 7-8 minutes per order, which translates into a 14.8% increase in daily picking operations capacity of the warehouse.
The research was partially supported by the Natural Sciences and Engineering Research Council of Canada and the Ontario Center of Excellence.

Short Bio

Vedat Bayram is an Assistant Professor at TED University, Department of Industrial Engineering. Prior to his current position he was a Postdoctoral Research Fellow at the University of Waterloo, Faculty of Engineering, Department of Management Sciences. He worked for many years as a Senior Operations Research Analysis Officer at the Project Management Division of Turkish General Staff Headquarters. He received his PhD. from Bilkent University, the Department of Industrial Engineering in 2015. He holds an M.S. degree in Operations Research from U.S. Naval Postgraduate School and a B.S. degree in Systems Engineering from Turkish Army Academy. His research interests include large scale optimization, convex optimization, stochastic programming, and data analytics applications with an emphasis on disaster management, transportation and logistics problems


Friday, October 16, 2020, 4.00 pm - Webex Meeting


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