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A Set-Partitioning-Based Model for the Stochastic Vehicle Routing Problem (2006)
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BibTeX
@MISC{Novoa06aset-partitioning-based,
author = {Clara Novoa and Rosemary Berger and Jeff Linderoth and Robert Storer},
title = {A Set-Partitioning-Based Model for the Stochastic Vehicle Routing Problem},
year = {2006}
}
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Abstract
The objective of the Vehicle Routing Problem (VRP) is to construct a minimum cost set of vehicle routes that visits all customers and satisfies demands without violating the vehicle capacity constraints. The Stochastic Vehicle Routing Problem (SVRP) results when one or more elements of the VRP are modeled as random variables. In this paper, we present a set-partitioning-based modeling framework for the VRP with stochastic demands (VRPSD). The framework can be adapted easily for routing problems with randomness in other problem elements, such as random customers and random travel times. We formulate the VRPSD as a two-stage stochastic program and introduce an extended recourse strategy in which vehicles are allowed to serve additional customers from failed routes prior to returning to the depot or to serve customers from failed routes on a new route after returning to the depot. Computational experiments show that route plans generated using the new recourse function perform quite well, especially for problems with few customers per route, where cost savings of roughly 5 % are possible. 1
Keyphrases
stochastic vehicle routing problem set-partitioning-based model random customer vehicle capacity constraint computational experiment cost saving additional customer satisfies demand stochastic demand vehicle routing problem two-stage stochastic program extended recourse strategy new route random variable problem element minimum cost set random travel time route plan set-partitioning-based modeling framework new recourse function perform vehicle route
