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Stowage decisions in multi-zone storage systems

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  • Rong Yuan
  • Tolga Cezik
  • Stephen C. Graves

Abstract

The stowage decision determines how arriving products are distributed in a storage system or warehouse. In particular, we consider the zone-stowage decision for large warehouses that are organised into distinct storage zones. An example would be a multi-floor warehouse where each floor is a storage zone. Each storage zone has limited picking capacity; we want to stow the product inventory across the storage zones so as to be able to meet uncertain demand requirements with the limited picking capacity in each zone. Determining how to spread the inventory across the storage zones is the zone-stowage decision that we consider in this paper. With a simulation study, we identify two zone-stowage policies that are effective in balancing the picking workload across different storage zones. The first zone-stowage policy achieves a chaining-inspired allocation by splitting the received quantity for each product across two storage zones; the second zone-stowage policy explicitly tracks the expected workload for each storage zone, termed the velocity of the zone, and then stows arriving products to the storage zone with the smallest velocity.

Suggested Citation

  • Rong Yuan & Tolga Cezik & Stephen C. Graves, 2018. "Stowage decisions in multi-zone storage systems," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 333-343, January.
    • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:1-2:p:333-343
    DOI: 10.1080/00207543.2017.1398428
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    Citations

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    Cited by:

    1. Boysen, Nils & de Koster, René & Weidinger, Felix, 2019. "Warehousing in the e-commerce era: A survey," European Journal of Operational Research, Elsevier, vol. 277(2), pages 396-411.
    2. Lam, H.Y. & Ho, G.T.S. & Mo, Daniel Y. & Tang, Valerie, 2023. "Responsive pick face replenishment strategy for stock allocation to fulfil e-commerce order," International Journal of Production Economics, Elsevier, vol. 264(C).
    3. Hengle Qin & Jun Xiao & Dongdong Ge & Linwei Xin & Jianjun Gao & Simai He & Haodong Hu & John Gunnar Carlsson, 2022. "JD.com: Operations Research Algorithms Drive Intelligent Warehouse Robots to Work," Interfaces, INFORMS, vol. 52(1), pages 42-55, January.
    4. Onal, Sevilay & Zhu, Wen & Das, Sanchoy, 2023. "Order picking heuristics for online order fulfillment warehouses with explosive storage," International Journal of Production Economics, Elsevier, vol. 256(C).
    5. Gharehgozli, Amir & Zaerpour, Nima, 2020. "Robot scheduling for pod retrieval in a robotic mobile fulfillment system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    6. Li, Xiaowei & Hua, Guowei & Huang, Anqiang & Sheu, Jiuh-Biing & Cheng, T.C.E. & Huang, Fengquan, 2020. "Storage assignment policy with awareness of energy consumption in the Kiva mobile fulfilment system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    7. Russell Allgor & Tolga Cezik & Daniel Chen, 2023. "Algorithm for Robotic Picking in Amazon Fulfillment Centers Enables Humans and Robots to Work Together Effectively," Interfaces, INFORMS, vol. 53(4), pages 266-282, July.
    8. Zhang, Jingran & Onal, Sevilay & Das, Sanchoy, 2020. "The dynamic stocking location problem – Dispersing inventory in fulfillment warehouses with explosive storage," International Journal of Production Economics, Elsevier, vol. 224(C).
    9. Jianming Cai & Xiaokang Li & Yue Liang & Shan Ouyang, 2021. "Collaborative Optimization of Storage Location Assignment and Path Planning in Robotic Mobile Fulfillment Systems," Sustainability, MDPI, vol. 13(10), pages 1-26, May.

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