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Propagation of unit location uncertainty in dense storage environments

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  • Patrick J. Reilly
  • Jennifer A. Pazour
  • Kellie R. Schneider

Abstract

Dense storage systems provide high-space utilisation; however, because not all units are immediately accessible, selectively offloading units can require shifting of other stored units in order to access the requested unit. Given an initial certainty in unit location, a discrete time Markov Chain is developed to quantify the growth of unit location uncertainty as a function of retrieval requests. As the first to mathematically model uncertainty propagation in dense storage operations, metrics are developed to analyse the model. A theoretical understanding of the relationship among storage density, retrieval times and unit location uncertainty is provided. Finally, a case study using inventory and load plan data from a military application illustrates how the developed models can be used by managers to evaluate selective offloading policies and layouts.

Suggested Citation

  • Patrick J. Reilly & Jennifer A. Pazour & Kellie R. Schneider, 2017. "Propagation of unit location uncertainty in dense storage environments," International Journal of Production Research, Taylor & Francis Journals, vol. 55(18), pages 5435-5449, September.
  • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:18:p:5435-5449
    DOI: 10.1080/00207543.2017.1319582
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    Cited by:

    1. Mofidi, Seyed Shahab & Pazour, Jennifer A. & Roy, Debjit, 2018. "Proactive vs. reactive order-fulfillment resource allocation for sea-based logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 66-84.
    2. Jianglong Yang & Li Zhou & Huwei Liu, 2021. "Hybrid genetic algorithm-based optimisation of the batch order picking in a dense mobile rack warehouse," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-25, April.

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