On the use of Monte Carlo simulation, cache and splitting techniques to improve the clarke and wright savings heuristics
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Tipus de documentArticle
Data publicació2011
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Abstract
This paper presents the SR-GCWS-CS probabilistic algorithm that combines Monte Carlo simulation with
splitting techniques and the Clarke and Wright savings heuristic to find competitive quasi-optimal solutions to
the Capacitated Vehicle Routing Problem (CVRP) in reasonable response times. The algorithm, which does
not require complex fine-tuning processes, can be used as an alternative to other metaheuristics—such as
Simulated Annealing, Tabu Search, Genetic Algorithms, Ant Colony Optimization or GRASP, which might be
more difficult to implement and which might require non-trivial fine-tuning processes—when solving CVRP
instances. As discussed in the paper, the probabilistic approach presented here aims to provide a relatively
simple and yet flexible algorithm which benefits from: (a) the use of the geometric distribution to guide the
random search process, and (b) efficient cache and splitting techniques that contribute to significantly reduce
computational times. The algorithm is validated through a set of CVRP standard benchmarks and competitive
results are obtained in all tested cases. Future work regarding the use of parallel programming to efficiently
solve large-scale CVRP instances is discussed. Finally, it is important to notice that some of the principles
of the approach presented here might serve as a base to develop similar algorithms for other routing and
scheduling combinatorial problems.
CitacióJuan, A. [et al.]. On the use of Monte Carlo simulation, cache and splitting techniques to improve the clarke and wright savings heuristics. "Journal of the Operational Research Society", 2011, vol. 62, núm. 6, p. 1085-1097.
ISSN0160-5682
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