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A polling-based dynamic order picking system for online retailers

Author

Listed:
  • René de Koster

    (EM - EMLyon Business School)

  • Yeming Gong

Abstract

One of the challenging questions that online retailers are currently facing is how to organize the logistic fulfillment processes both during and after a transaction has taken place. As new information technologies become available that allow picking information to be conveyed in real time and with the ongoing need to create greater responsiveness to customers, there is increasing interest in applying dynamic picking in the warehouses of online retailers. In a Dynamic Picking System (DPS), a worker picks orders that arrive in real time during the picking operations and the picking information can dynamically change in a picking cycle. Models to describe and analyze such systems via stochastic polling theory are presented and closed-form expressions for the order line waiting times in a DPS are derived. These analytical results are verified by simulation. It is shown that the application of polling-based picking can generally lead to shorter order throughput times and higher on-time service completion ratios than traditional batch picking systems using optimal batch sizes. It is demonstrated that the proposed analysis method can be applied to minimize warehouse cost and improve service.

Suggested Citation

  • René de Koster & Yeming Gong, 2008. "A polling-based dynamic order picking system for online retailers," Post-Print hal-02312476, HAL.
    • Handle: RePEc:hal:journl:hal-02312476
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    Citations

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    Cited by:

    1. 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.
    2. Minfang Huang & Qiong Guo & Jing Liu & Xiaoxu Huang, 2018. "Mixed Model Assembly Line Scheduling Approach to Order Picking Problem in Online Supermarkets," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
    3. Vellian Vatumalae & Premkumar Rajagopal & Veera Pandiyan Kaliani Sundram, 2020. "Warehouse Management System of a Third Party Logistics Provider in Malaysia," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 12(9), pages 1-73, September.
    4. 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.
    5. 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.
    6. Erdem Çeven & Kevin R. Gue, 2017. "Optimal Wave Release Times for Order Fulfillment Systems with Deadlines," Transportation Science, INFORMS, vol. 51(1), pages 52-66, February.
    7. Claeys, Dieter & Adan, Ivo & Boxma, Onno, 2016. "Stochastic bounds for order flow times in parts-to-picker warehouses with remotely located order-picking workstations," European Journal of Operational Research, Elsevier, vol. 254(3), pages 895-906.
    8. Yu, Mengfei & de Koster, René B.M., 2009. "The impact of order batching and picking area zoning on order picking system performance," European Journal of Operational Research, Elsevier, vol. 198(2), pages 480-490, October.
    9. Fangyu Chen & Hongwei Wang & Yong Xie & Chao Qi, 2016. "An ACO-based online routing method for multiple order pickers with congestion consideration in warehouse," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 389-408, April.
    10. Stef Moons & Katrien Ramaekers & An Caris & Yasemin Arda, 2018. "Integration of order picking and vehicle routing in a B2C e-commerce context," Flexible Services and Manufacturing Journal, Springer, vol. 30(4), pages 813-843, December.
    11. Kateryna Czerniachowska & Radosław Wichniarek & Krzysztof Żywicki, 2023. "A Model for an Order-Picking Problem with a One-Directional Conveyor and Buffer," Sustainability, MDPI, vol. 15(18), pages 1-18, September.
    12. Hong, Soondo & Johnson, Andrew L. & Peters, Brett A., 2012. "Batch picking in narrow-aisle order picking systems with consideration for picker blocking," European Journal of Operational Research, Elsevier, vol. 221(3), pages 557-570.
    13. Xu, Haoxuan & Gong, Yeming (Yale) & Chu, Chengbin & Zhang, Jinlong, 2017. "Dynamic lot-sizing models for retailers with online channels," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 171-184.
    14. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    15. Chen, Heng & Hu, Zhangchen & Solak, Senay, 2021. "Improved delivery policies for future drone-based delivery systems," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1181-1201.
    16. Lu, Wenrong & McFarlane, Duncan & Giannikas, Vaggelis & Zhang, Quan, 2016. "An algorithm for dynamic order-picking in warehouse operations," European Journal of Operational Research, Elsevier, vol. 248(1), pages 107-122.
    17. 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.
    18. Boysen, Nils & de Koster, René & Füßler, David, 2021. "The forgotten sons: Warehousing systems for brick-and-mortar retail chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 361-381.

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