| Téléchargement | - Voir la version finale : Hierarchical reinforcement learning for vehicle routing problems with time windows (PDF, 317 Kio)
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| DOI | Trouver le DOI : https://doi.org/10.21428/594757db.f0516e23 |
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| Auteur | Rechercher : Wang, Yunli1; Rechercher : Sun, Sun1; Rechercher : Li, Wei |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | 34th Canadian Conference on Artificial Intelligence, Canadian AI 2021, May 25-28, 2021, online |
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| Sujet | vehicle routing problem with time windows; hierarchical reinforcement learning; generalization |
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| Résumé | Vehicle routing problem with time windows (VRPTW) is a practical and complex vehicle routing problem (VRP) which is faced by thousands of companies in logistics and transportation. Usually, VRP is solved by traditional heuristic algorithms. Recently, deep learning models under the reinforcement learning (RL) framework have been proposed to solve variants of VRP. In our study, we propose to use the hierarchical RL to find an optimal policy for generating optimal routes in VRPTW. The hierarchical RL structure includes a low level which generates feasible solutions and a high level which further searches for an optimal solution. Experimental results show that the proposed hierarchical RL model outperforms the non-hierarchical RL model and the heuristic algorithms Google OR-Tools. The proposed model also shows generalization capability in three different scenarios: varied time window constraints, from small-scale to large-scale problems, and generalization across different datasets. The flexible framework of hierarchical RL can also be applied to solve other complex VRPs with multiple objectives. |
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| Date de publication | 2021-06-08 |
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| Maison d’édition | Canadian Artificial Intelligence Association |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | e02634fa-53d9-4666-8876-5db877efe04a |
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| Enregistrement créé | 2021-08-11 |
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| Enregistrement modifié | 2021-08-11 |
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