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An interventionist strategy for warehouse order picking: Evidence from two case studies

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  • Giannikas, Vaggelis
  • Lu, Wenrong
  • Robertson, Brian
  • McFarlane, Duncan

Abstract

As the role of the customer becomes more important in modern logistics, warehouses are required to improve their response to customer orders. To meet the responsiveness expected by customers, warehouses need to shorten completion times. In this paper, we introduce an interventionist order picking strategy that aims to improve the responsiveness of order picking systems. Unlike existing dynamic strategies, the proposed strategy allows a picker to be intervened during a pick cycle to consider new orders and operational disruptions. An interventionist strategy is compared against an existing dynamic picking strategy via a case study. We report benefits both in terms of order completion time and travel distance. This paper also introduces a set of system requirements for deploying an interventionist strategy based on a further case study.

Suggested Citation

  • Giannikas, Vaggelis & Lu, Wenrong & Robertson, Brian & McFarlane, Duncan, 2017. "An interventionist strategy for warehouse order picking: Evidence from two case studies," International Journal of Production Economics, Elsevier, vol. 189(C), pages 63-76.
    • Handle: RePEc:eee:proeco:v:189:y:2017:i:c:p:63-76
    DOI: 10.1016/j.ijpe.2017.04.002
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    2. Dalia Perkumienė & Kristina Ratautaitė & Rasa Pranskūnienė, 2022. "Innovative Solutions and Challenges for the Improvement of Storage Processes," Sustainability, MDPI, vol. 14(17), pages 1-21, August.
    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. 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.
    5. 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).
    6. MacCarthy, Bart L. & Zhang, Lina & Muyldermans, Luc, 2019. "Best Performance Frontiers for Buy-Online-Pickup-in-Store order fulfilment," International Journal of Production Economics, Elsevier, vol. 211(C), pages 251-264.
    7. Gharehgozli, Amir & Zaerpour, Nima, 2020. "Robot scheduling for pod retrieval in a robotic mobile fulfillment system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    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. Zhuang, Yanling & Zhou, Yun & Hassini, Elkafi & Yuan, Yufei & Hu, Xiangpei, 2022. "Rack retrieval and repositioning optimization problem in robotic mobile fulfillment systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    10. Lining Xing & Yuanyuan Liu & Haiyan Li & Chin-Chia Wu & Win-Chin Lin & Xin Chen, 2020. "A Novel Tabu Search Algorithm for Multi-AGV Routing Problem," Mathematics, MDPI, vol. 8(2), pages 1-16, February.
    11. 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.

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