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A grouping genetic algorithm for the Order Batching Problem in distribution warehouses

Author

Listed:
  • Sören Koch

    (Otto-von-Guericke-Universität Magdeburg)

  • Gerhard Wäscher

    (Otto-von-Guericke-Universität Magdeburg
    Beijing Jiaotong University)

Abstract

Order picking is a warehouse function that deals with the retrieval of articles from their storage locations in order to satisfy certain customer demands. Combining several single customer orders into one (more substantial) picking order can increase the efficiency of warehouse operations. The Order Batching Problem considered in this paper deals with the question of how different customer orders should be grouped into picking orders, such that the total length of all tours through the warehouse is minimized, which are necessary to collect all requested articles. For the solution of this problem, the authors introduce a grouping genetic algorithm. It is combined with a local search procedure which results in a highly competitive hybrid algorithm. In a series of extensive numerical experiments, the algorithm is benchmarked against a genetic algorithm with a standard item-oriented encoding scheme. The results show that the new genetic algorithm based on the group-oriented encoding scheme is preferable for the Order Batching Problem, and that the algorithm provides high quality solutions in reasonable computing times.

Suggested Citation

  • Sören Koch & Gerhard Wäscher, 2016. "A grouping genetic algorithm for the Order Batching Problem in distribution warehouses," Journal of Business Economics, Springer, vol. 86(1), pages 131-153, January.
    • Handle: RePEc:spr:jbecon:v:86:y:2016:i:1:d:10.1007_s11573-015-0789-x
    DOI: 10.1007/s11573-015-0789-x
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    References listed on IDEAS

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    11. Sebastian Henn & Sören Koch & Karl Doerner & Christine Strauss & Gerhard Wäscher, 2009. "Metaheuristics for the Order Batching Problem in Manual Order Picking Systems," FEMM Working Papers 09020, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
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    Cited by:

    1. Pardo, Eduardo G. & Gil-Borrás, Sergio & Alonso-Ayuso, Antonio & Duarte, Abraham, 2024. "Order batching problems: Taxonomy and literature review," European Journal of Operational Research, Elsevier, vol. 313(1), pages 1-24.
    2. Boysen, Nils & de Koster, René & Weidinger, Felix, 2019. "Warehousing in the e-commerce era: A survey," European Journal of Operational Research, Elsevier, vol. 277(2), pages 396-411.
    3. Wagner, Stefan & Mönch, Lars, 2023. "A variable neighborhood search approach to solve the order batching problem with heterogeneous pick devices," European Journal of Operational Research, Elsevier, vol. 304(2), pages 461-475.
    4. Çağla Cergibozan & A. Serdar Tasan, 2022. "Genetic algorithm based approaches to solve the order batching problem and a case study in a distribution center," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 137-149, January.
    5. Žulj, Ivan & Kramer, Sergej & Schneider, Michael, 2018. "A hybrid of adaptive large neighborhood search and tabu search for the order-batching problem," European Journal of Operational Research, Elsevier, vol. 264(2), pages 653-664.
    6. Sergio Gil-Borrás & Eduardo G. Pardo & Antonio Alonso-Ayuso & Abraham Duarte, 2020. "GRASP with Variable Neighborhood Descent for the online order batching problem," Journal of Global Optimization, Springer, vol. 78(2), pages 295-325, October.

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    More about this item

    Keywords

    Warehouse management; Order picking; Order batching; Genetic algorithms;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • M1 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration
    • L8 - Industrial Organization - - Industry Studies: Services
    • M19 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Other

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