Conveyor Merges in Zone Picking Systems: A Tractable and Accurate Approximate Model

Sequential zone picking systems are popular conveyor-based picker-to-parts order picking systems that divide the order picking area in work zones. When designing a zone picking system, it is important to know whether the throughput capability of the system can meet customer demand. However, the performance and maximum throughput capability of a zone picking system is largely determined by congestion and blocking that occur at the various conveyor merges in the system. In this paper the authors develop an analytical model to study the impact of conveyor merges in sequential zone picking systems. Because of finite buffers, blocking, recirculation, and merging, the resulting queueing model does not have a product-form stationary queue-length distribution which makes exact analysis practically infeasible. Therefore, the authors develop an approximate solution by using an aggregation technique and matrix-geometric methods to study the throughput capability of the system. The model is suitable to support rapid design of complex zone picking systems, in terms of number and length of zones, input and output buffer capacities, and storage allocation of products to zones to meet prespecified performance targets. Comparison of the approximation results to simulation show that for a wide range of parameters the mean relative error in the system throughput is typically less than 5%. The model accurately predicts the loss in throughput due to congestion and blocking at the merges, and can be used to allocate input and output buffer spaces to maximize the throughput capability of the system.

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    • Abstracts reprinted with permission of INFORMS (Institute for Operations Research and the Management Sciences, http://www.informs.org).
  • Authors:
    • van der Gaast, J P
    • de Koster, M B M
    • Adan, I J B F
  • Publication Date: 2018-11

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  • English

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  • Accession Number: 01691447
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Dec 28 2018 10:48AM