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Vehicle Routing with Stochastic Demands: Models & Computational Methods

In: Modeling Uncertainty

Author

Listed:
  • Moshe Dror

    (The University of Arizona)

Abstract

In this paper we provide an overview and modeling details regarding vehicle routing in situations in which customer demand is revealed only when the vehicle arrives at the customer’s location. Given a fixed capacity vehicle, this setting gives rise to the possibility that the vehicle on arrival does not have sufficient inventory to completely supply a given customer’s demand. Such an occurrence is called a route failure and it requires additional vehicle trips to fully replenish such a customer. Given a set of customers, the objective is to design vehicle routes and response policies which minimize the expected delivery cost by a fleet of fixed capacity vehicles. We survey the different problem statements and formulations. In addition, we describe a number of the algorithmic developments for constructing routing solutions. Primarily we focus on stochastic programming models with different recourse options. We also present a Markov decision approach for this problem and conclude with a cha llenging conjecture regarding finite sums of random variables.

Suggested Citation

  • Moshe Dror, 2002. "Vehicle Routing with Stochastic Demands: Models & Computational Methods," International Series in Operations Research & Management Science, in: Moshe Dror & Pierre L’Ecuyer & Ferenc Szidarovszky (ed.), Modeling Uncertainty, chapter 0, pages 625-649, Springer.
    • Handle: RePEc:spr:isochp:978-0-306-48102-4_25
    DOI: 10.1007/0-306-48102-2_25
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    Citations

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    Cited by:

    1. Nicola Secomandi & François Margot, 2009. "Reoptimization Approaches for the Vehicle-Routing Problem with Stochastic Demands," Operations Research, INFORMS, vol. 57(1), pages 214-230, February.
    2. E. Angelelli & R. Mansini & M. Vindigni, 2016. "The Stochastic and Dynamic Traveling Purchaser Problem," Transportation Science, INFORMS, vol. 50(2), pages 642-658, May.
    3. Anupam Gupta & Viswanath Nagarajan & R. Ravi, 2012. "Technical Note---Approximation Algorithms for VRP with Stochastic Demands," Operations Research, INFORMS, vol. 60(1), pages 123-127, February.
    4. Luca Bertazzi & Nicola Secomandi, 2020. "Technical Note—Worst-Case Benefit of Restocking for the Vehicle Routing Problem with Stochastic Demands," Operations Research, INFORMS, vol. 68(3), pages 671-675, May.
    5. Bertazzi, Luca & Secomandi, Nicola, 2018. "Faster rollout search for the vehicle routing problem with stochastic demands and restocking," European Journal of Operational Research, Elsevier, vol. 270(2), pages 487-497.

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