Application of the Nested Rollout Policy Adaptation Algorithm to the Traveling Salesman Problem with Time Windows

Tristan Cazenave (1) , Fabien Teytaud (1, 2)

1 LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision
2 HEC Paris - Ecole des Hautes Etudes Commerciales

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.

Mots clés: Nested Monte-Carlo; Nested Rollout Policy Adaptation; Trav- eling Salesman Problem with Time Windows

Domaines: Intelligence artificielle [cs.AI]; Recherche opérationnelle [math.OC]

Tristan Cazenave, Fabien Teytaud. Application of the Nested Rollout Policy Adaptation Algorithm to the Traveling Salesman Problem with Time Windows. LION, Jan 2012, Paris, France. pp.42 - 54, ⟨10.1007/978-3-642-34413-8_4⟩. ⟨hal-01406457⟩