Dynamic vehicle routing with stochastic requests

Authors:
Russell Bent;Pascal Van Hentenryck
Affiliations:
Brown University;Brown University
Venue:
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Year:
2003

Citing 3
Cited 7

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Performance analysis of online anticipatory algorithms for large multistage stochastic integer programs

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A comparative study between dynamic adapted PSO and VNS for the vehicle routing problem with dynamic requests

Applied Soft Computing
Self-organizing maps in population based metaheuristic to the dynamic vehicle routing problem

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Abstract

This paper considers vehicle routing problems (VRP) where customer locations and service times are random variables that are realized dynamically during plan execution. It proposes a multiple scenario approach (MSA) that continuously generates plans consistent with past decisions and anticipating future requests. The approach, which combines AI and OR techniques in novel ways, is compared with the best available heuristics that model long-distance courier mail services [Larsen et al, 2002]. Experimental results shows that MSA may significantly decrease travel times and is robust wrt reasonably noisy distributions.