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Robust possibilistic programming for joint order batching and picker routing problem in warehouse management

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  • Mahdi Yousefi Nejad Attari
  • Ali Ebadi Torkayesh
  • Behnam Malmir
  • Ensiyeh Neyshabouri Jami

Abstract

Decisions made for designing and operating a warehouse system are of great significance. These operational decisions are strongly affected by total logistics costs, including investment and direct operating costs. The number of orders made by customers in the logistics section of warehouse management is very high because the number, type of products and items ordered by different customers vary broadly. However, machines layout for picking up products at logistics centres is minimal, inflexible, and, in some cases, inconclusive. In this study, we address joint order batching procedures of orders considering picker routing problem as a mixed-integer programming model. Extensive numerical experiments were generated in small, medium, and large sizes. In order to consider the uncertainty of parameters, we applied robust possibilistic programming for this problem. Three different meta-heuristic algorithms; genetic algorithm, particle swarm optimisation algorithm, and honey artificial bee colony algorithms are used as solution approaches to solve the formulated model. The performance of solution approaches over the problem was analysed using several test indexes. In all three group examples, there was no significant difference among mean values of the objective function, while there was a remarkable difference among computing times.

Suggested Citation

  • Mahdi Yousefi Nejad Attari & Ali Ebadi Torkayesh & Behnam Malmir & Ensiyeh Neyshabouri Jami, 2021. "Robust possibilistic programming for joint order batching and picker routing problem in warehouse management," International Journal of Production Research, Taylor & Francis Journals, vol. 59(14), pages 4434-4452, July.
    • Handle: RePEc:taf:tprsxx:v:59:y:2021:i:14:p:4434-4452
    DOI: 10.1080/00207543.2020.1766712
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    Cited by:

    1. Dhirendra Prajapati & M. Manoj Kumar & Saurabh Pratap & H. Chelladurai & Mohd Zuhair, 2021. "Sustainable Logistics Network Design for Delivery Operations with Time Horizons in B2B E-Commerce Platform," Logistics, MDPI, vol. 5(3), pages 1-13, September.
    2. Pardo, Eduardo G. & Gil-Borrás, Sergio & Alonso-Ayuso, Antonio & Duarte, Abraham, 2024. "Order batching problems: Taxonomy and literature review," European Journal of Operational Research, Elsevier, vol. 313(1), pages 1-24.
    3. Alireza Goli & Ali Ala & Seyedali Mirjalili, 2023. "A robust possibilistic programming framework for designing an organ transplant supply chain under uncertainty," Annals of Operations Research, Springer, vol. 328(1), pages 493-530, September.
    4. Grzegorz Tarczyński, 2023. "Linear programming models for optimal workload and batching in pick-and-pass warehousing systems," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 33(3), pages 141-158.
    5. Kamilla Hamre Bolstad & Manu Joshi & Lars Magnus Hvattum & Magnus Stålhane, 2022. "Composing Vessel Fleets for Maintenance at Offshore Wind Farms by Solving a Dual-Level Stochastic Programming Problem Using GRASP," Logistics, MDPI, vol. 6(1), pages 1-22, January.

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