A multi-space sampling heuristic for the vehicle routing problem with stochastic demands
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The vehicle routing problem with stochastic demands consists in designing transportation routes of minimal expected cost to satisfy a set of customers with random demands of known probability distributions. This paper proposes a simple yet effective heuristic approach that uses randomized heuristics for the traveling salesman problem, a tour partitioning procedure, and a set partitioning formulation to sample the solution space and find high-quality solutions for the problem. Computational experiments on benchmark instances from the literature show that the proposed approach is competitive with the state-of-the-art algorithm for the problem in terms of both accuracy and efficiency. In experiments conducted on a set of 40 instances, the proposed approach unveiled four new best-known solutions (BKSs) and matched another 24. For the remaining 12 instances, the heuristic reported average gaps with respect to the BKS ranging from 0.69 to 0.15 % depending on its configuration.
KeywordsVehicle routing Stochastic demands Heuristics Sampling Logistics
The authors would like to thank two anonymous referees for insightful comments and valuable advice that helped to improve the paper. This research was partially funded by Region Pays de la Loire (France) through project LigéRO and by the Universidad de Antioquia (Colombia) through project: Desarrollo de metaheurísticos híbridos y métodos cooperativos para problemas de rutas de vehículos con flota heterogénea y restricciones adicionales.
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