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Policy adaptation for vehicle routing

Cazenave, Tristan; Lucas, Jean-Yves; Triboulet, Thomas; Kim, Hyoseok (2021), Policy adaptation for vehicle routing, AI Communications, 34, 1, p. 21-35. 10.3233/AIC-201577

Type
Article accepté pour publication ou publié
Date
2021
Nom de la revue
AI Communications
Volume
34
Numéro
1
Éditeur
IOS Press
Pages
21-35
Identifiant publication
Métadonnées
Afficher la notice complète
Auteur(s)
Cazenave, Tristan
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Lucas, Jean-Yves
Optimisation, Simulation, Risque et Statistiques pour les Marchés de l’Energie [EDF R&D OSIRIS]
Triboulet, Thomas
Optimisation, Simulation, Risque et Statistiques pour les Marchés de l’Energie [EDF R&D OSIRIS]
Kim, Hyoseok
Optimisation, Simulation, Risque et Statistiques pour les Marchés de l’Energie [EDF R&D OSIRIS]
Résumé (EN)
Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo search algorithm that learns a playout policy in order to solve a single player game. In this paper we apply NRPA to the vehicle routing problem. This problem is important for large companies that have to manage a fleet of vehicles on a daily basis. Real problems are often too large to be solved exactly. The algorithm is applied to standard problem of the literature and to the specific problems of EDF (Electricité De France, the main French electric utility company). These specific problems have peculiar constraints. NRPA gives better result than the algorithm previously used by EDF.
Mots-clés
Vehicle Routing Problems; Capacitated Vehicle Routing with Time Windows; Nested Rollout Policy Adaptation