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Mathematical model, heuristics and exact method for order picking in narrow aisles

Author

Listed:
  • Thomas Chabot
  • Leandro C. Coelho
  • Jacques Renaud
  • Jean-François Côté

Abstract

Order picking is one of the most challenging operations in distribution centre management and one of the most important sources of costs. One way to reduce the lead time and associated costs is to minimise the total amount of work for collecting all orders. This paper is motivated by a collaboration with an industrial partner who delivers furniture and electronic equipment. We have modelled their narrow aisles order picking problem as a vehicle routing problem through a series of distance transformations between all pairs of locations. Security issues arising when working on narrow aisles impose an extra layer of difficulty when determining the routes. We show that these security measures and the operator equipment allow us to decompose the problem per aisle. In other words, if one has to pick orders from three aisles in the warehouse, it is possible to decompose the problem and create three different instances of the picking problem. Our approach yields an exact representation of all possible picking sequences. We also show that neglecting 2D aspects and solving the problem over a 1D warehouse yields significant difference in the solutions, which are then suboptimal for the real 2D case. We have solved a large set of instances reproducing realistic configurations using a combination of heuristics and an exact algorithm, minimising the total distance travelled for picking all items. Through extensive computational experiments, we identify which of our methods are better suited for each aisle configuration. We also compare our solutions with those obtained by the company order picking procedures, showing that improvements can be achieved by using our approach.

Suggested Citation

  • Thomas Chabot & Leandro C. Coelho & Jacques Renaud & Jean-François Côté, 2018. "Mathematical model, heuristics and exact method for order picking in narrow aisles," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(8), pages 1242-1253, August.
  • Handle: RePEc:taf:tjorxx:v:69:y:2018:i:8:p:1242-1253
    DOI: 10.1080/01605682.2017.1390532
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    Citations

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    Cited by:

    1. Arbex Valle, Cristiano & Beasley, John E, 2020. "Order batching using an approximation for the distance travelled by pickers," European Journal of Operational Research, Elsevier, vol. 284(2), pages 460-484.
    2. 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.
    3. van Gils, Teun & Caris, An & Ramaekers, Katrien & Braekers, Kris & de Koster, René B.M., 2019. "Designing efficient order picking systems: The effect of real-life features on the relationship among planning problems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 47-73.
    4. Polten, Lukas & Emde, Simon, 2021. "Scheduling automated guided vehicles in very narrow aisle warehouses," Omega, Elsevier, vol. 99(C).
    5. Neves-Moreira, Fábio & Amorim, Pedro, 2024. "Learning efficient in-store picking strategies to reduce customer encounters in omnichannel retail," International Journal of Production Economics, Elsevier, vol. 267(C).
    6. Jose Alejandro Cano & Pablo Cortés & Jesús Muñuzuri & Alexander Correa-Espinal, 2023. "Solving the picker routing problem in multi-block high-level storage systems using metaheuristics," Flexible Services and Manufacturing Journal, Springer, vol. 35(2), pages 376-415, June.
    7. Silva, Allyson & Coelho, Leandro C. & Darvish, Maryam & Renaud, Jacques, 2020. "Integrating storage location and order picking problems in warehouse planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).

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