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Metaheuristics for the Order Batching Problem in Manual Order Picking Systems

Author

Listed:
  • Sebastian Henn

    (Faculty of Economics and Management, Otto-von-Guericke University Magdeburg)

  • Sören Koch

    (Faculty of Economics and Management, Otto-von-Guericke University Magdeburg)

  • Karl Doerner

    (Faculty of Economics and Management, Otto-von-Guericke University Magdeburg)

  • Christine Strauss
  • Gerhard Wäscher

    (Faculty of Economics and Management, Otto-von-Guericke University Magdeburg)

Abstract

In manual order picking systems, order pickers walk or drive through a distribution warehouse in order to collect items which are requested by (internal or external) customers. In order to perform these operations effciently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic approaches for the solution of this problem; the rst one is based on Iterated Local Search, the second one on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed approaches are benchmarked against classic solution methods. It is demonstrated that the proposed methods are not only superior to existing methods, but provide solutions which may allow for operating distribution warehouses signi cantly more effcient.

Suggested Citation

  • 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.
  • Handle: RePEc:mag:wpaper:09020
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    References listed on IDEAS

    as
    1. Pan, C-H. & Liu, S-Y., 1995. "A comparative study of order batching algorithms," Omega, Elsevier, vol. 23(6), pages 691-700, December.
    2. Gibson, David R. & Sharp, Gunter P., 1992. "Order batching procedures," European Journal of Operational Research, Elsevier, vol. 58(1), pages 57-67, April.
    3. Chen, Mu-Chen & Wu, Hsiao-Pin, 2005. "An association-based clustering approach to order batching considering customer demand patterns," Omega, Elsevier, vol. 33(4), pages 333-343, August.
    4. Stutzle, Thomas, 2006. "Iterated local search for the quadratic assignment problem," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1519-1539, November.
    5. G. Clarke & J. W. Wright, 1964. "Scheduling of Vehicles from a Central Depot to a Number of Delivery Points," Operations Research, INFORMS, vol. 12(4), pages 568-581, August.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Çağla Cergibozan & A. Serdar Tasan, 2019. "Order batching operations: an overview of classification, solution techniques, and future research," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 335-349, January.
    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. 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.
    4. Sören Koch & Gerhard Wäscher, 2011. "A Grouping Genetic Algorithm for the Order Batching Problem in Distribution Warehouses," FEMM Working Papers 110026, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    5. Sebastian Henn, 2012. "Variable Neighborhood Search for the Order Batching and Sequencing Problem with Multiple Pickers," FEMM Working Papers 120004, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    6. A. Scholz & G. Wäscher, 2017. "Order Batching and Picker Routing in manual order picking systems: the benefits of integrated routing," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(2), pages 491-520, June.
    7. Gerhard Wäscher & André Scholz, 2015. "A Solution Approach for the Joint Order Batching and Picker Routing Problem in a Two-Block Layout," FEMM Working Papers 150004, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    8. Sandra Hahn & André Scholz, 2017. "Order Picking in Narrow-Aisle Warehouses: A Fast Approach to Minimize Waiting Times," FEMM Working Papers 170006, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    9. Sebastian Henn & Verena Schmid, 2011. "Metaheuristics for Order Batching and Sequencing in Manual Order Picking Systems," FEMM Working Papers 110011, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    10. Anderson Rogério Faia Pinto & Marcelo Seido Nagano, 2020. "Genetic algorithms applied to integration and optimization of billing and picking processes," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 641-659, March.
    11. 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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