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Solving the multiple level warehouse layout problem using ant colony optimization

Author

Listed:
  • Jean-Paul Arnaout

    (Gulf University for Science and Technology)

  • Caline ElKhoury

    (Lebanese American University)

  • Gamze Karayaz

    (Işik University)

Abstract

This paper addresses the multiple level warehouse layout problem, which involves assigning items to cells and levels with the objective of minimizing transportation costs. A monthly demand and an inventory requirement are associated with every item type along with vertical and horizontal unit transportation costs. The warehouse has one port to transport items vertically from ground floor to the other levels, where each item must be assigned to exactly one cell on the assigned level. An ant colony optimization (ACO) algorithm is adapted to this NP-complete problem and its performance is evaluated by comparing its solutions to the ones obtained using genetic algorithms (GA) as well as the optimal solutions for small problems. The computational results reflected the superiority of ACO in large-size problem instances, with a marginally better performance than GA in smaller ones, while solving the tested instances within a reasonable computational time. Furthermore, ACO was able to attain most of the known optimal solutions for small-size problem instances.

Suggested Citation

  • Jean-Paul Arnaout & Caline ElKhoury & Gamze Karayaz, 2020. "Solving the multiple level warehouse layout problem using ant colony optimization," Operational Research, Springer, vol. 20(1), pages 473-490, March.
  • Handle: RePEc:spr:operea:v:20:y:2020:i:1:d:10.1007_s12351-017-0334-5
    DOI: 10.1007/s12351-017-0334-5
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    References listed on IDEAS

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    Cited by:

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    2. Kuang-Hua Hu & Ming-Fu Hsu & Fu-Hsiang Chen & Mu-Ziyun Liu, 2021. "Identifying the key factors of subsidiary supervision and management using an innovative hybrid architecture in a big data environment," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.

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