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Capacity Analysis of Sequential Zone Picking Systems

Author

Listed:
  • Jelmer P. van der Gaast

    (Department of Management Science, Fudan University, 200086 Shanghai, China)

  • René B. M. de Koster

    (Department of Management of Technology & Innovation, Erasmus Universiteit)

  • Ivo J. B. F. Adan

    (Department of Industrial Engineering & Innovation Sciences, Technische Universiteit, 5612 AZ Eindhoven, Netherlands)

  • Jacques A. C. Resing

    (Department of Mathematics and Computer Science, Technische Universiteit, 5612 AZ Eindhoven, Netherlands)

Abstract

This paper develops a capacity model for sequential zone picking systems. These systems are popular internal transport and order-picking systems because of their scalability, flexibility, high-throughput ability, and fit for use for a wide range of products and order profiles. The major disadvantage of such systems is congestion and blocking under heavy use, leading to long order throughput times. To reduce blocking and congestion, most systems use the block-and-recirculate protocol to dynamically manage workload. In this paper, the various elements of the system, such as conveyor lanes and pick zones, are modeled as a multiclass block-and-recirculate queueing network with capacity constraints on subnetworks. Because of this blocking protocol, the stationary distribution of the queueing network is highly intractable. We propose an approximation method based on jump-over blocking. Multiclass jump-over queueing networks admit a product-form stationary distribution and can be efficiently evaluated by mean value analysis and Norton’s theorem. This method can be applied during the design phase of sequential zone picking systems to determine the number of segments, number and length of zones, buffer capacities, and storage allocation of products to zones to meet performance targets. For a wide range of parameters, the results show that the relative error in the system throughput is typically less than 1% compared with simulation.

Suggested Citation

  • Jelmer P. van der Gaast & René B. M. de Koster & Ivo J. B. F. Adan & Jacques A. C. Resing, 2020. "Capacity Analysis of Sequential Zone Picking Systems," Operations Research, INFORMS, vol. 68(1), pages 161-179, January.
    • Handle: RePEc:inm:oropre:v:68:y:2020:i:1:p:161-179
    DOI: 10.1287/opre.2019.1885
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    References listed on IDEAS

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    Cited by:

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    2. van der Gaast, Jelmer Pier & Weidinger, Felix, 2022. "A deep learning approach for the selection of an order picking system," European Journal of Operational Research, Elsevier, vol. 302(2), pages 530-543.
    3. Zheng Wang & Jiuh‐Biing Sheu & Chung‐Piaw Teo & Guiqin Xue, 2022. "Robot Scheduling for Mobile‐Rack Warehouses: Human–Robot Coordinated Order Picking Systems," Production and Operations Management, Production and Operations Management Society, vol. 31(1), pages 98-116, January.
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    5. David Winkelmann & Frederik Tolkmitt & Matthias Ulrich & Michael Romer, 2022. "Integrated storage assignment for an e-grocery fulfilment centre: Accounting for day-of-week demand patterns," Papers 2209.03998, arXiv.org, revised May 2023.
    6. Boysen, Nils & de Koster, René & Füßler, David, 2021. "The forgotten sons: Warehousing systems for brick-and-mortar retail chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 361-381.

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