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Consolidation strategies for the delivery of perishable products

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  • Nguyen, Christine
  • Dessouky, Maged
  • Toriello, Alejandro

Abstract

A set of agricultural suppliers with low demands can save on long-haul transportation costs by consolidating their product. We consider a system with stochastic demand and a single consolidation point near the suppliers. We propose a look-ahead heuristic that takes advantage of economies of scale by aiming to ship larger quantities. We experimentally compare the heuristic’s performance against other simple policies, a rolling horizon algorithm, and a stochastic dynamic programming model. Our numerical results demonstrate that the heuristic provides solutions that are near the lower bound provided by the dynamic programming model, and that the benefits of consolidating depend on the size of the suppliers’ demand. We also propose a proportional cost allocation rule that encourages the suppliers to cooperate with each other instead of operating independently.

Suggested Citation

  • Nguyen, Christine & Dessouky, Maged & Toriello, Alejandro, 2014. "Consolidation strategies for the delivery of perishable products," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 69(C), pages 108-121.
    • Handle: RePEc:eee:transe:v:69:y:2014:i:c:p:108-121
    DOI: 10.1016/j.tre.2014.05.018
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    4. Arroyo, Federico, 2024. "Cost Allocation in Vehicle Routing Problems with Time Windows," Junior Management Science (JUMS), Junior Management Science e. V., vol. 9(1), pages 1241-1268.
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    6. Wang, Min & Zhao, Lindu & Herty, Michael, 2019. "Joint replenishment and carbon trading in fresh food supply chains," European Journal of Operational Research, Elsevier, vol. 277(2), pages 561-573.
    7. Song, Yang & Fan, Tijun & Tang, Yuewu & Xu, Chang, 2021. "Omni-channel strategies for fresh produce with extra losses in-store," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 148(C).
    8. Cook, Robert A. & Lodree, Emmett J., 2017. "Dispatching policies for last-mile distribution with stochastic supply and demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 353-371.
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