Learn, Compare, Search: One Sawmill’s Search for the Best Cutting Patterns Across and/or Trees

Author:

Ménard Marc-AndréORCID,Morin MichaelORCID,Khachan MohammedORCID,Gaudreault JonathanORCID,Quimper Claude-GuyORCID

Publisher

Springer International Publishing

Reference33 articles.

1. Alvarez, A.M., Louveaux, Q., Wehenkel, L.: A machine learning-based approximation of strong branching. INFORMS J. Comput. 29(1), 185–195 (2017)

2. Antuori, V., Hébrard, E., Huguet, M.J., Essodaigui, S., Nguyen, A.: Combining Monte Carlo tree search and depth first search methods for a car manufacturing workshop scheduling problem. In: International Conference on Principles and Practice of Constraint Programming (CP) (2021)

3. Bachiri, I., Gaudreault, J., Quimper, C.G., Chaib-draa, B.: RLBS: an adaptive backtracking strategy based on reinforcement learning for combinatorial optimization. In: 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 936–942. IEEE (2015)

4. Beck, J.C.: Solution-guided multi-point constructive search for job shop scheduling. J. Artif. Intell. Res. 29, 49–77 (2007)

5. Boussemart, F., Hemery, F., Lecoutre, C., Sais, L.: Boosting systematic search by weighting constraints. In: Proceedings of the 16th European Conference on Artificial Intelligence (ECAI), vol. 16 (2004)

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