Open Access
Wuhan Univ. J. Nat. Sci.
Volume 27, Number 6, December 2022
Page(s) 531 - 538
Published online 10 January 2023
  1. Taillard E. Benchmarks for basic scheduling problems [J]. European Journal of Operational Research, 1993, 64(2): 278-285. [CrossRef] [Google Scholar]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. Schulman J, Wolski F, Dhariwal P, et al. Proximal policy optimization algorithms [EB/OL]. [2022-09-10]. [Google Scholar]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]. [Google Scholar]
  13. 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]
  14. 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]
  15. 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]
  16. Schaul T, Quan J, Antonoglou I, et al. Prioritized experience replay [EB/OL]. [2022-09-10]. [Google Scholar]
  17. Fortunato M, Azar M G, Piot B, et al. Noisy networks for exploration [EB/OL]. [2022-09-10]. [Google Scholar]
  18. Lawrence S. Supplement to Resource Constrained Project Scheduling: An Experimental Investigation of Heuristic Scheduling Techniques [R]. Pittsburgh: Carnegie Mellon University, 1984. [Google Scholar]
  19. 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]
  20. 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]
  21. 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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.