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Product allocation problem for an AS/RS with multiple in-the-aisle pick positions

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  • Faraz Ramtin
  • Jennifer A. Pazour

Abstract

An automated storage/retrieval system with multiple in-the-aisle pick positions is a semi-automated case-level order fulfillment technology that is widely used in distribution centers. We study the impact of product to pick position assignments on the expected throughput for different operating policies, demand profiles, and shape factors. We develop efficient algorithms of complexity O(nlog(n)) that provide the assignment that minimizes the expected travel time. Also, for different operating policies, shape configurations, and demand curves, we explore the structure of the optimal assignment of products to pick positions and quantify the difference between using a simple, practical assignment policy versus the optimal assignment. Finally, we derive closed-form analytical travel time models by approximating the optimal assignment's expected travel time using continuous demand curves and assuming an infinite number of pick positions in the aisle. We illustrate that these continuous models work well in estimating the travel time of a discrete rack and use them to find optimal design configurations.

Suggested Citation

  • Faraz Ramtin & Jennifer A. Pazour, 2015. "Product allocation problem for an AS/RS with multiple in-the-aisle pick positions," IISE Transactions, Taylor & Francis Journals, vol. 47(12), pages 1379-1396, December.
    • Handle: RePEc:taf:uiiexx:v:47:y:2015:i:12:p:1379-1396
    DOI: 10.1080/0740817X.2015.1027458
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    Citations

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    Cited by:

    1. Dong, Wenquan & Jin, Mingzhou, 2024. "Automated storage and retrieval system design with variant lane depths," European Journal of Operational Research, Elsevier, vol. 314(2), pages 630-646.
    2. Gharehgozli, Amir & Xu, Chao & Zhang, Wenda, 2021. "High multiplicity asymmetric traveling salesman problem with feedback vertex set and its application to storage/retrieval system," European Journal of Operational Research, Elsevier, vol. 289(2), pages 495-507.
    3. Tappia, Elena & Roy, Debjit & Melacini, Marco & De Koster, René, 2019. "Integrated storage-order picking systems: Technology, performance models, and design insights," European Journal of Operational Research, Elsevier, vol. 274(3), pages 947-965.
    4. Shahab Derhami & Jeffrey S. Smith & Kevin R. Gue, 2017. "Optimising space utilisation in block stacking warehouses," International Journal of Production Research, Taylor & Francis Journals, vol. 55(21), pages 6436-6452, November.
    5. Polten, Lukas & Emde, Simon, 2022. "Multi-shuttle crane scheduling in automated storage and retrieval systems," European Journal of Operational Research, Elsevier, vol. 302(3), pages 892-908.
    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.
    7. Lanza, Giacomo & Passacantando, Mauro & Scutellà, Maria Grazia, 2022. "Assigning and sequencing storage locations under a two level storage policy: Optimization model and matheuristic approaches," Omega, Elsevier, vol. 108(C).
    8. Derhami, Shahab & Smith, Jeffrey S. & Gue, Kevin R., 2020. "A simulation-based optimization approach to design optimal layouts for block stacking warehouses," International Journal of Production Economics, Elsevier, vol. 223(C).
    9. Kaveh Azadeh & René De Koster & Debjit Roy, 2019. "Robotized and Automated Warehouse Systems: Review and Recent Developments," Transportation Science, INFORMS, vol. 53(4), pages 917-945, July.

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