Combining Monte Carlo Tree Search and Depth First Search Methods for a Car Manufacturing Workshop Scheduling Problem
| Autor: | Antuori, Valentin, Hebrard, Emmanuel, Huguet, Marie-José, Essodaigui, Siham, Nguyen, Alain |
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| Přispěvatelé: | Équipe Recherche Opérationnelle, Optimisation Combinatoire et Contraintes (LAAS-ROC), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, RENAULT, ANR-19-P3IA-0004,ANITI,Artificial and Natural Intelligence Toulouse Institute(2019), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Monte-Carlo Tree Search
Scheduling Travelling Salesman Problem Mathematics of computing → Combinatorial optimization Computing methodologies → Planning and scheduling Computing methodologies → Discrete space search [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO] Mathematics of computing → Combinatoric problems [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
| Zdroj: | 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). International Conference on Principles and Practice of Constraint Programming International Conference on Principles and Practice of Constraint Programming, Oct 2021, Montpellier (on line), France. ⟨10.4230/LIPIcs.CP.2021.14⟩ |
| DOI: | 10.4230/LIPIcs.CP.2021.14⟩ |
| Popis: | Many state-of-the-art methods for combinatorial games rely on Monte Carlo Tree Search (MCTS) method, coupled with machine learning techniques, and these techniques have also recently been applied to combinatorial optimization. In this paper, we propose an efficient approach to a Travelling Salesman Problem with time windows and capacity constraints from the automotive industry. This approach combines the principles of MCTS to balance exploration and exploitation of the search space and a backtracking method to explore promising branches, and to collect relevant information on visited subtrees. This is done simply by replacing the Monte-Carlo rollouts by budget-limited runs of a DFS method. Moreover, the evaluation of the promise of a node in the Monte-Carlo search tree is key, and is a major difference with the case of games. For that purpose, we propose to evaluate a node using the marginal increase of a lower bound of the objective function, weighted with an exponential decay on the depth, in previous simulations. Finally, since the number of Monte-Carlo rollouts and hence the confidence on the evaluation is higher towards the root of the search tree, we propose to adjust the balance exploration/exploitation to the length of the branch. Our experiments show that this method clearly outperforms the best known approaches for this problem. LIPIcs, Vol. 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021), pages 14:1-14:16 |
| Databáze: | OpenAIRE |
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