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Managing stochastic demand in an Inventory Routing Problem with transportation procurement

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  • Bertazzi, Luca
  • Bosco, Adamo
  • Laganà, Demetrio

Abstract

We study an Inventory Routing Problem in which the supplier has a limited production capacity and the stochastic demand of the retailers is satisfied with procurement of transportation services. The aim is to minimize the total expected cost over a planning horizon, given by the sum of the inventory cost at the supplier, the inventory cost at the retailers, the penalty cost for stock-out at the retailers and the transportation cost. First, we show that a policy based just on the average demand can have a total expected cost infinitely worse than the one obtained by taking into account the overall probability distribution of the demand in the decision process. Therefore, we introduce a stochastic dynamic programming formulation of the problem that allows us to find an optimal policy in small size instances. Finally, we design and implement a matheuristic approach, integrating a rollout algorithm and an optimal solution of mixed-integer linear programming models, which is able to solve realistic size problem instances. Computational results allow us to provide managerial insights concerning the management of stochastic demand.

Suggested Citation

  • Bertazzi, Luca & Bosco, Adamo & Laganà, Demetrio, 2015. "Managing stochastic demand in an Inventory Routing Problem with transportation procurement," Omega, Elsevier, vol. 56(C), pages 112-121.
  • Handle: RePEc:eee:jomega:v:56:y:2015:i:c:p:112-121
    DOI: 10.1016/j.omega.2014.09.010
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    7. A. Mor & M. G. Speranza, 2020. "Vehicle routing problems over time: a survey," 4OR, Springer, vol. 18(2), pages 129-149, June.
    8. Wadi Khalid Anuar & Lai Soon Lee & Hsin-Vonn Seow & Stefan Pickl, 2022. "A Multi-Depot Dynamic Vehicle Routing Problem with Stochastic Road Capacity: An MDP Model and Dynamic Policy for Post-Decision State Rollout Algorithm in Reinforcement Learning," Mathematics, MDPI, vol. 10(15), pages 1-70, July.
    9. A. Mor & M. G. Speranza, 2022. "Vehicle routing problems over time: a survey," Annals of Operations Research, Springer, vol. 314(1), pages 255-275, July.
    10. Mosca, Alyssa & Vidyarthi, Navneet & Satir, Ahmet, 2019. "Integrated transportation – inventory models: A review," Operations Research Perspectives, Elsevier, vol. 6(C).
    11. Rossi, Roberto & Tomasella, Maurizio & Martin-Barragan, Belen & Embley, Tim & Walsh, Christopher & Langston, Matthew, 2019. "The Dynamic Bowser Routing Problem," European Journal of Operational Research, Elsevier, vol. 275(1), pages 108-126.
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    13. Beltran-Royo, C., 2017. "Two-stage stochastic mixed-integer linear programming: The conditional scenario approach," Omega, Elsevier, vol. 70(C), pages 31-42.
    14. Bertazzi, Luca & Bosco, Adamo & Laganà, Demetrio, 2016. "Min–Max exact and heuristic policies for a two-echelon supply chain with inventory and transportation procurement decisions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 57-70.
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