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Turnover-based storage in non-traditional unit-load warehouse designs

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  • Letitia Pohl
  • Russell Meller
  • Kevin Gue

Abstract

This article investigates the effect of assigning the most-active items to the best locations in unit-load warehouses with non-traditional aisles. Specifically, the performance of flying-V and fishbone designs are investigated when products exhibit different velocity profiles. Both single- and dual-command operations are considered for a warehouse where receiving and shipping are located at the midpoint of one side of the warehouse. For dual-command operations, a fishbone design shows similar reductions in travel distances for both random and turnover-based storage policies. The fishbone designs that provide the best performance have a diagonal cross aisle that extends to the upper corners of the picking space and are approximately half as tall as they are wide. In general, warehouse design parameters that perform best under random storage also perform well under turnover-based storage.

Suggested Citation

  • Letitia Pohl & Russell Meller & Kevin Gue, 2011. "Turnover-based storage in non-traditional unit-load warehouse designs," IISE Transactions, Taylor & Francis Journals, vol. 43(10), pages 703-720.
    • Handle: RePEc:taf:uiiexx:v:43:y:2011:i:10:p:703-720
    DOI: 10.1080/0740817X.2010.549098
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    Citations

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    Cited by:

    1. Yeliz Kocaman & Ömer Öztürkoğlu & Şevkinaz Gümüşoğlu, 2021. "Aisle designs in unit-load warehouses with different flow policies of multiple pickup and deposit points," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(1), pages 323-355, March.
    2. Subir S. Rao & Gajendra K. Adil, 2017. "Analytical models for a new turnover-based hybrid storage policy in unit-load warehouses," International Journal of Production Research, Taylor & Francis Journals, vol. 55(2), pages 327-346, January.
    3. Rakesh Venkitasubramony & Gajendra K. Adil, 2016. "Analytical models for pick distances in fishbone warehouse based on exact distance contour," International Journal of Production Research, Taylor & Francis Journals, vol. 54(14), pages 4305-4326, July.
    4. Li Zhou & Huwei Liu & Junhui Zhao & Fan Wang & Jianglong Yang, 2022. "Performance Analysis of Picking Routing Strategies in the Leaf Layout Warehouse," Mathematics, MDPI, vol. 10(17), pages 1-28, September.
    5. Ang, Marcus & Lim, Yun Fong, 2019. "How to optimize storage classes in a unit-load warehouse," European Journal of Operational Research, Elsevier, vol. 278(1), pages 186-201.
    6. Masae, Makusee & Glock, Christoph H. & Vichitkunakorn, Panupong, 2021. "A method for efficiently routing order pickers in the leaf warehouse," International Journal of Production Economics, Elsevier, vol. 234(C).
    7. Öztürkoğlu, Ö. & Gue, K.R. & Meller, R.D., 2014. "A constructive aisle design model for unit-load warehouses with multiple pickup and deposit points," European Journal of Operational Research, Elsevier, vol. 236(1), pages 382-394.
    8. Cardona, Luis F. & Soto, Diego F. & Rivera, Leonardo & Martínez, Hector J., 2015. "Detailed design of fishbone warehouse layouts with vertical travel," International Journal of Production Economics, Elsevier, vol. 170(PC), pages 825-837.
    9. Diefenbach, Heiko & Grosse, Eric H. & Glock, Christoph H., 2024. "Human-and-cost-centric storage assignment optimization in picker-to-parts warehouses," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1049-1068.
    10. Öztürkoğlu, Ömer & Hoser, Deniz, 2019. "A discrete cross aisle design model for order-picking warehouses," European Journal of Operational Research, Elsevier, vol. 275(2), pages 411-430.

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