In this paper, we are interested in the minimization of the travel cost of the traveling salesman problem with time windows. In order to do this minimization we use a Nested Rollout Policy Adaptation (NRPA) algorithm. NRPA has multiple levels and maintains the best tour at each level. It consists in learning a rollout policy at each level. We also show how to improve the original algorithm with a modified rollout policy that helps NRPA to avoid time windows violations. © 2012 Springer-Verlag.
CITATION STYLE
Cazenave, T., & Teytaud, F. (2012). Application of the nested rollout policy adaptation algorithm to the traveling salesman problem with time windows. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7219 LNCS, pp. 42–54). https://doi.org/10.1007/978-3-642-34413-8_4
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