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Controlling distribution inventory systems with shipment consolidation and compound Poisson demand

Author

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  • Johansson, Lina
  • Sonntag, Danja R.
  • Marklund, Johan
  • Kiesmüller, Gudrun P.

Abstract

We consider a one-warehouse-multiple-retailer inventory system where the retailers face stochastic customer demand, modelled as compound Poisson processes. Deliveries from the central warehouse to groups of retailers are consolidated using a time based shipment consolidation policy. This means that replenishment orders have to wait until a vehicle departures, which increases the lead time for the retailers and therefore also the safety stock. Thus, a trade-off exists between expected shipment costs and holding costs. Our aim is to determine the shipment intervals and the required amount of safety stock for each retailer and the warehouse to minimize total cost, both for backorder costs and fill rate constraints. Previous work has focused on exact solutions which are computationally demanding and not applicable for larger real world problems. The focus of our present work is on the development of computationally attractive heuristics that can be applied in practice. A numerical study shows that the proposed heuristics perform well compared to the exact cost minimizing solutions. We also illustrate that the approaches are appropriate for solving real world problems using data from a large European company.

Suggested Citation

  • Johansson, Lina & Sonntag, Danja R. & Marklund, Johan & Kiesmüller, Gudrun P., 2020. "Controlling distribution inventory systems with shipment consolidation and compound Poisson demand," European Journal of Operational Research, Elsevier, vol. 280(1), pages 90-101.
    • Handle: RePEc:eee:ejores:v:280:y:2020:i:1:p:90-101
    DOI: 10.1016/j.ejor.2019.06.045
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    Citations

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    Cited by:

    1. Peng, Xiaoshuai & Zhang, Lele & Thompson, Russell G. & Wang, Kangzhou, 2023. "A three-phase heuristic for last-mile delivery with spatial-temporal consolidation and delivery options," International Journal of Production Economics, Elsevier, vol. 266(C).
    2. Yajun Zhan & Yiping Jiang, 2022. "Integrated Optimization of Order Allocation and Last-Mile Multi-Temperature Joint Distribution for Fresh Agriproduct Community Retail," Sustainability, MDPI, vol. 14(15), pages 1-18, August.
    3. Romero-Silva, Rodrigo & Mujica Mota, Miguel, 2022. "Trade-offs in the landside operations of air cargo hubs: Horizontal cooperation and shipment consolidation policies considering capacitated nodes," Journal of Air Transport Management, Elsevier, vol. 103(C).
    4. Dimitrova, Dimitrina S. & Ignatov, Zvetan G. & Kaishev, Vladimir K. & Tan, Senren, 2020. "On double-boundary non-crossing probability for a class of compound processes with applications," European Journal of Operational Research, Elsevier, vol. 282(2), pages 602-613.
    5. Prak, Dennis & Teunter, Ruud & Babai, Mohamed Zied & Boylan, John E. & Syntetos, Aris, 2021. "Robust compound Poisson parameter estimation for inventory control," Omega, Elsevier, vol. 104(C).
    6. Jana Ralfs & Gudrun P. Kiesmüller, 2022. "Inventory management with advance demand information and flexible shipment consolidation," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(4), pages 1009-1044, December.
    7. Wang, Qinan & Wan, Guangyu, 2023. "Fixed-interval order-up-to policies and myopic optimal warehouse stock allocation for one-warehouse multiple-retailer systems," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1112-1124.
    8. Sonntag, Danja R. & Schrotenboer, Albert H. & Kiesmüller, Gudrun P., 2023. "Stochastic inventory routing with time-based shipment consolidation," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1186-1201.

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