Open Access
Issue |
Wuhan Univ. J. Nat. Sci.
Volume 27, Number 6, December 2022
|
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Page(s) | 531 - 538 | |
DOI | https://doi.org/10.1051/wujns/2022276531 | |
Published online | 10 January 2023 |
- Taillard E. Benchmarks for basic scheduling problems [J]. European Journal of Operational Research, 1993, 64(2): 278-285. [CrossRef] [Google Scholar]
- Vela C R, Afsar S, Palacios J J, et al. Evolutionary tabu search for flexible due-date satisfaction in fuzzy job shop scheduling [J]. Computers & Operations Research, 2020, 119: 104931. [CrossRef] [MathSciNet] [Google Scholar]
- Liu S C, Chen Z G, Zhan Z H, et al. Many-objective job-shop scheduling: A multiple populations for multiple objectives-based genetic algorithm approach [J]. IEEE Transactions on Cybernetics, 2021: 1-15. DOI: 10.1109/TCYB.2021.3102642. [Google Scholar]
- Ding H J, Gu X S. Improved particle swarm optimization algorithm based novel encoding and decoding schemes for flexible job shop scheduling problem [J]. Computers & Operations Research, 2020, 121: 104951. [CrossRef] [MathSciNet] [Google Scholar]
- Ni F, Hao J Y, Lu J W, et al. A multi-graph attributed reinforcement learning based optimization algorithm for large-scale hybrid flow shop scheduling problem [C]// Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. New York: ACM, 2021: 3441-3451. [Google Scholar]
- Mnih V, Kavukcuoglu K, Silver D, et al. Human-level control through deep reinforcement learning [J]. Nature, 2015, 518(7540): 529-533. [NASA ADS] [CrossRef] [Google Scholar]
- Schulman J, Wolski F, Dhariwal P, et al. Proximal policy optimization algorithms [EB/OL]. [2022-09-10]. https://arxiv.org/abs/1707.06347. [Google Scholar]
- Lin C C, Deng D J, Chih Y L, et al. Smart manufacturing scheduling with edge computing using multiclass deep Q network [J]. IEEE Transactions on Industrial Informatics, 2019, 15(7): 4276-4284. [CrossRef] [Google Scholar]
- Zhang C, Song W, Cao Z G, et al. Learning to dispatch for job shop scheduling via deep reinforcement learning [C]//Proceedings of the 34th International Conference on Neural Information Processing Systems. New York: ACM, 2020: 1621-1632. [Google Scholar]
- Park J, Chun J, Kim S H, et al. Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning[J]. International Journal of Production Research, 2021, 59(11): 3360-3377. [CrossRef] [Google Scholar]
- Han B A, Yang J J. Research on adaptive job shop scheduling problems based on dueling double DQN [J]. IEEE Access, 2020, 8: 186474-186495. [CrossRef] [Google Scholar]
- Zeng Y H, Liao Z J, Dai Y Z, et al. Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanism [EB/OL]. [2022-09-10]. https://arxiv.org/abs/2201.00548. [Google Scholar]
- Hessel M, Modayil J, van Hasselt H, et al. Rainbow: Combining improvements in deep reinforcement learning [J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2018, 32(1): 3215-3222. [CrossRef] [Google Scholar]
- van Hasselt H, Guez A, Silver D. Deep reinforcement learning with double Q-Learning [C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence. New York: ACM, 2016: 2094-2100. [Google Scholar]
- Wang Z Y, Schaul T, Hessel M, et al. Dueling network architectures for deep reinforcement learning [C]// Proceedings of the 33rd International Conference on International Conference on Machine Learning. New York: ACM, 2016: 1995-2003. [Google Scholar]
- Schaul T, Quan J, Antonoglou I, et al. Prioritized experience replay [EB/OL]. [2022-09-10]. https://arxiv.org/abs/1511.05952. [Google Scholar]
- Fortunato M, Azar M G, Piot B, et al. Noisy networks for exploration [EB/OL]. [2022-09-10]. https://arxiv.org/abs/1706.10295. [Google Scholar]
- Lawrence S. Supplement to Resource Constrained Project Scheduling: An Experimental Investigation of Heuristic Scheduling Techniques [R]. Pittsburgh: Carnegie Mellon University, 1984. [Google Scholar]
- Applegate D, Cook W, Bonn U, et al. A Computational Study of the Job-Shop Scheduling Problem [M]. Bonn: Rheinische Friedrich-Wilhelms-Universität, 1990. [Google Scholar]
- Storer R H, Wu S D, Vaccari R. New search spaces for sequencing problems with application to job shop scheduling [J]. Management Science, 1992, 38(10): 1495-1509. [CrossRef] [Google Scholar]
- Gen M, Tsujimura Y, Kubota E. Solving job-shop scheduling problems by genetic algorithm [C]// Proceedings of IEEE International Conference on Systems, Man and Cybernetics. New York: IEEE, 1994: 1577-1582. [Google Scholar]
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