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

Bayram, Vedat, Baloch, Gohram, Gzara, Fatma, Elhedhli, Samir (2022) Optimal Order Batching in Warehouse Management: A Data-Driven Robust Approach. INFORMS Journal on Optimization, 4 (3). pp. 278-303. ISSN 2575-1484. E-ISSN 2575-1492. (doi:10.1287/ijoo.2021.0066) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:99709)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL:
https://doi.org/10.1287/ijoo.2021.0066

Abstract

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) that groups orders into batches to minimize total order processing time accounting for uncertainty caused by system congestion and human behavior. We provide a generalizable, data-driven approach that overcomes warehouse-specific assumptions characterizing most of the work in the literature. We analyze historical data to understand the processes in the warehouse, to predict processing times, and to improve order processing. We introduce the ROBP and develop an efficient learning-based branch-and-price algorithm based on simultaneous column and row generation, embedded with alternative prediction models such as linear regression and random forest that 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. The data-driven prescriptive analytics tool we propose achieves savings of seven to eight minutes per order, which translates into a 14.8% increase in daily picking operations capacity of the warehouse.

Item Type: Article
DOI/Identification number: 10.1287/ijoo.2021.0066
Uncontrolled keywords: warehouse management; order batching; batch processing; data analytics
Subjects: H Social Sciences > HF Commerce > HF5351 Business
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Funders: TED University (https://ror.org/0285rh439)
Depositing User: Vedat Bayram
Date Deposited: 26 Jan 2023 09:58 UTC
Last Modified: 04 Mar 2024 15:38 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/99709 (The current URI for this page, for reference purposes)

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