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Utilizing individual picker skills to improve order batching in a warehouse

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  • Matusiak, Marek
  • de Koster, René
  • Saarinen, Jari

Abstract

Batching orders and routing order pickers is a commonly studied problem in many picker-to-parts warehouses. The impact of individual differences in picking skills on performance has received little attention. In this paper, we show that we are able to improve state-of-the-art batching and routing methods by almost 10% taking skill differences among pickers into account in minimizing the sum of total order processing time. Compared to assigning order batches to pickers only based on individual picker productivity, savings of 6% in total time are achieved. The increase in picker productivity depends on the picker category, but values of over 16% are observed for some categories. We demonstrate this for the case of a Finnish retailer. First, using time-stamped picking data, multilevel modeling is used to forecast batch execution times for individual pickers by modeling individual skills of pickers. Next, these forecasts are used to minimize total batch execution time, by assigning the right picker to the right order batch. We formulate the problem as a joint order batching and generalized assignment model, and solve it with an Adaptive Large Neighborhood Search algorithm.

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  • Matusiak, Marek & de Koster, René & Saarinen, Jari, 2017. "Utilizing individual picker skills to improve order batching in a warehouse," European Journal of Operational Research, Elsevier, vol. 263(3), pages 888-899.
    • Handle: RePEc:eee:ejores:v:263:y:2017:i:3:p:888-899
    DOI: 10.1016/j.ejor.2017.05.002
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    Cited by:

    1. Arpan Rijal & Marco Bijvank & Asvin Goel & René de Koster, 2021. "Workforce Scheduling with Order-Picking Assignments in Distribution Facilities," Transportation Science, INFORMS, vol. 55(3), pages 725-746, May.
    2. 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.
    3. 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.
    4. XiaoLi Zhang & Jelle de Vries & René de Koster & ChenGuang Liu, 2022. "Fast and Faultless? Quantity and Quality Feedback in Order Picking," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1536-1559, April.
    5. Kaibo Liang & Li Zhou & Jianglong Yang & Huwei Liu & Yakun Li & Fengmei Jing & Man Shan & Jin Yang, 2023. "Research on a Dynamic Task Update Assignment Strategy Based on a “Parts to Picker” Picking System," Mathematics, MDPI, vol. 11(7), pages 1-29, March.
    6. Loske, Dominic & Klumpp, Matthias & Grosse, Eric H. & Modica, Tiziana & Glock, Christoph H., 2023. "Storage systems’ impact on order picking time: An empirical economic analysis of flow-rack storage systems," International Journal of Production Economics, Elsevier, vol. 261(C).
    7. Žulj, Ivan & Salewski, Hagen & Goeke, Dominik & Schneider, Michael, 2022. "Order batching and batch sequencing in an AMR-assisted picker-to-parts system," European Journal of Operational Research, Elsevier, vol. 298(1), pages 182-201.
    8. Zhong, Shuya & Giannikas, Vaggelis & Merino, Jorge & McFarlane, Duncan & Cheng, Jun & Shao, Wei, 2022. "Evaluating the benefits of picking and packing planning integration in e-commerce warehouses," European Journal of Operational Research, Elsevier, vol. 301(1), pages 67-81.
    9. van Gils, Teun & Caris, An & Ramaekers, Katrien & Braekers, Kris, 2019. "Formulating and solving the integrated batching, routing, and picker scheduling problem in a real-life spare parts warehouse," European Journal of Operational Research, Elsevier, vol. 277(3), pages 814-830.
    10. Kuhn, Heinrich & Schubert, Daniel & Holzapfel, Andreas, 2021. "Integrated order batching and vehicle routing operations in grocery retail – A General Adaptive Large Neighborhood Search algorithm," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1003-1021.
    11. 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.
    12. Shandong Mou, 2022. "Integrated Order Picking and Multi-Skilled Picker Scheduling in Omni-Channel Retail Stores," Mathematics, MDPI, vol. 10(9), pages 1-19, April.
    13. van Gils, Teun & Ramaekers, Katrien & Caris, An & de Koster, René B.M., 2018. "Designing efficient order picking systems by combining planning problems: State-of-the-art classification and review," European Journal of Operational Research, Elsevier, vol. 267(1), pages 1-15.
    14. Othmane Benmoussa, 2022. "Improving Replenishment Flows Using Simulation Results: A Case Study," Logistics, MDPI, vol. 6(2), pages 1-26, May.
    15. 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.
    16. Antonio Maria Coruzzolo & Francesco Lolli & Elia Balugani & Elisa Magnani & Miguel Afonso Sellitto, 2023. "Order Picking Problem: A Model for the Joint Optimisation of Order Batching, Batch Assignment Sequencing, and Picking Routing," Logistics, MDPI, vol. 7(3), pages 1-18, September.

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