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Robust Storage Assignment in Warehouses with Correlated Demand

  • Monika Kofler
  • Andreas Beham
  • Stefan Wagner
  • Michael Affenzeller
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 595)

Abstract

In many warehouses manual order picking is one of the most time and labour intensive processes. Products that are often ordered together are said to be correlated or affine and order picking performance may be improved by placing correlated products close to each other. In industries with strong seasonality patterns and fluctuating demand regular re-locations of products might be necessary to ensure that the quality of the storage assignment does not deteriorate over time. In this chapter we study how to generate more robust assignments that are suitable for volatile warehouse scenarios with correlated demand. In a case study based on 13 monthly snapshots from a real-world warehouse robust slotting outperformed greedy re-locations by up to 9.6 %.

Keywords

Greedy Approach Order Picking Robustness Measure Storage Assignment Material Handling Equipment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This paper is an updated and extended version of [12] and was first presented at the APCASE 2014 conference. The work described in this chapter was done within the Josef Ressel-Centre HEUREKA! for Heuristic Optimization sponsored by the Austrian Research Promotion Agency (FFG).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Monika Kofler
    • 1
  • Andreas Beham
    • 1
  • Stefan Wagner
    • 1
  • Michael Affenzeller
    • 1
  1. 1.Heuristic and Evolutionary Algorithms Laboratory (HEAL)University of Applied Sciences Upper AustriaHagenbergAustria

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