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Heuristic dispatching rules (HDRs) are widely used for solving the dynamic fuzzy job shop scheduling problem (DFJSSP).
O. Holthaus and C. Rajendran, “Efficient dispatching rules for scheduling in a job shop,” Int. J. Prod. Econ. , vol. 48, no. 1, pp. 87–105, 1997
1997
Earlier work this paper cites.
J. A. Gromicho, J. J. Van Hoorn, F. Saldanha-da Gama, and G. T. Timmer, “Solving the job-shop scheduling problem optimally by dynamic programming,” Comput. Oper. Res. , vol. 39, no. 12, pp. 2968–2977, 2012
2012
Earlier work this paper cites.
L. Nie, L. Gao, P. Li, and X. Shao, “Reactive scheduling in a job shop where jobs arrive over time,” Comput. Ind. Eng. , vol. 66, no. 2, pp. 389–405, 2013
2013
Earlier work this paper cites.
B. Wang, H. Xie, X. Xia, and X. Zhang, “A nsga-ii algorithm hybridizing local simulated-annealing operators for a bi-criteria robust job-shop scheduling problem under scenarios,” IEEE Trans. Fuzzy Syst. , vol. 27, no. 5, pp. 1075–1084, 2018
2018
Earlier work this paper cites.
M. DHurasević and D. Jakobović, “A survey of dispatching rules for the dynamic unrelated machines environment,” Expert Syst. Appl. , vol. 113, pp. 555–569, 2018
2018
Earlier work this paper cites.
L. Zhang, Z. Li, G. Królczyk, D. Wu, and Q. Tang, “Mathematical modeling and multi-attribute rule mining for energy efficient job-shop scheduling,” J. Cleaner Prod. , vol. 241, p. 118289, 2019
2019
Earlier work this paper cites.
D. Gao, G.-G. Wang, and W. Pedrycz, “Solving fuzzy job-shop scheduling problem using de algorithm improved by a selection mechanism,” IEEE Trans. Fuzzy Syst. , vol. 28, no. 12, pp. 3265–3275, 2020
2020
Earlier work this paper cites.
J.-q. Li, Z.-m. Liu, C. Li, and Z.-x. Zheng, “Improved artificial immune system algorithm for type-2 fuzzy flexible job shop scheduling problem,” IEEE Trans. Fuzzy Syst. , vol. 29, no. 11, pp. 3234–3248, 2020
2020
Earlier work this paper cites.
C. Zhang, W. Song, Z. Cao, J. Zhang, P. S. Tan, and X. Chi, “Learning to dispatch for job shop scheduling via deep reinforcement learning,” in Advances in Neural Information Processing Systems , vol. 33. Curran Associates, Inc., 2020, pp. 1621–1632
2020
Earlier work this paper cites.
G.-G. Wang, D. Gao, and W. Pedrycz, “Solving multiobjective fuzzy job-shop scheduling problem by a hybrid adaptive differential evolution algorithm,” IEEE Trans. Ind. Inf. , vol. 18, no. 12, pp. 8519–8528, 2022
2022
Earlier work this paper cites.
Y. Li, W. Gu, M. Yuan, and Y. Tang, “Real-time data-driven dynamic scheduling for flexible job shop with insufficient transportation resources using hybrid deep Q network,” Rob. Comput. Integr. Manuf. , vol. 74, p. 102283, 2022
2022
Earlier work this paper cites.
R. Liu, R. Piplani, and C. Toro, “Deep reinforcement learning for dynamic scheduling of a flexible job shop,” Int. J. Prod. Res. , vol. 60, no. 13, pp. 4049–4069, 2022
2022
Earlier work this paper cites.
S. Shady, T. Kaihara, N. Fujii, and D. Kokuryo, “Feature selection approach for evolving reactive scheduling policies for dynamic job shop scheduling problem using gene expression programming,” Int. J. Prod. Res. , vol. 61, no. 15, pp. 5029–5052, 2023
2023
Earlier work this paper cites.
H. Wang, T. Peng, A. Nassehi, and R. Tang, “A data-driven simulation-optimization framework for generating priority dispatching rules in dynamic job shop scheduling with uncertainties,” J. Manuf. Syst. , vol. 70, pp. 288–308, 2023
2023
Cited alongside, same era.
R. Chen, W. Li, and H. Yang, “A deep reinforcement learning framework based on an attention mechanism and disjunctive graph embedding for the job-shop scheduling problem,” IEEE Trans. Ind. Inf. , vol. 19, no. 2, pp. 1322–1331, 2023
2023
Cited alongside, same era.
L. Zhang, Y. Feng, Q. Xiao, Y. Xu, D. Li, D. Yang, and Z. Yang, “Deep reinforcement learning for dynamic flexible job shop scheduling problem considering variable processing times,” J. Manuf. Syst. , vol. 71, pp. 257–273, 2023
2023
Cited alongside, same era.
J. Xie, X. Li, L. Gao, and L. Gui, “A new neighbourhood structure for job shop scheduling problems,” Int. J. Prod. Res. , vol. 61, no. 7, pp. 2147–2161, 2023
2023
Cited alongside, same era.
C.-L. Liu, C.-J. Tseng, and P.-H. Weng, “Dynamic job-shop scheduling via graph attention networks and deep reinforcement learning,” IEEE Trans. Ind. Inf. , vol. 20, no. 6, pp. 8662–8672, 2024
2024
Closest in time.
L. Wan, L. Fu, C. Li, and K. Li, “Flexible job shop scheduling via deep reinforcement learning with meta-path-based heterogeneous graph neural network,” Knowledge-Based Syst. , vol. 296, p. 111940, 2024
2024
Closest in time.
S. Moon, S. Lee, and K.-J. Park, “Learning-enabled flexible job-shop scheduling for scalable smart manufacturing,” J. Manuf. Syst. , vol. 77, pp. 356–367, 2024
2024
Closest in time.
F. Liu, X. Tong, M. Yuan, X. Lin, F. Luo, Z. Wang, Z. Lu, and Q. Zhang, “An example of evolutionary computation + large language model beating human: Design of efficient guided local search,” arXiv.org , 2024
2024
Closest in time.
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2023
Cited alongside, same era.
N. Shinn, F. Cassano, A. Gopinath, K. Narasimhan, and S. Yao, “Reflexion: Language agents with verbal reinforcement learning,” Advances in Neural Information Processing Systems , vol. 36, pp. 8634–8652, 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Y. Yao, X. Li, and L. Gao, “A dqn-based memetic algorithm for energy-efficient job shop scheduling problem with integrated limited agvs,” Swarm Evol. Comput. , vol. 87, p. 101544, 2024
2024
Cited alongside, same era.
X. Wu, X. Yan, D. Guan, and M. Wei, “A deep reinforcement learning model for dynamic job-shop scheduling problem with uncertain processing time,” Eng. Appl. Artif. Intell. , vol. 131, p. 107790, 2024
2024
Cited alongside, same era.
B. Romera-Paredes, M. Barekatain, A. Novikov, M. Balog, M. P. Kumar, E. Dupont, F. J. R. Ruiz, J. S. Ellenberg, P. Wang, O. Fawzi, P. Kohli, and A. Fawzi, “Mathematical discoveries from program search with large language models,” Nature , vol. 625, no. 7995, pp. 468–475, 2024
2024
Cited alongside, same era.
H. Ye, J. Wang, Z. Cao, and G. Song, “Reevo: Large language models as hyper-heuristics with reflective evolution,” The Thirty-eighth Annual Conference on Neural Information Processing Systems , 2024
2024
Cited alongside, same era.
G. Mischler, Y. A. Li, S. Bickel, A. D. Mehta, and N. Mesgarani, “Contextual feature extraction hierarchies converge in large language models and the brain,” Nat. Mach. Intell. , pp. 1–11, 2024
2024
Closest in time.
J. Huang, X. Li, and L. Gao, “A novel ga-cp method for fixed-type multi-robot collaborative scheduling in flexible job shop,” IEEE Trans. Autom. Sci. Eng. , 2025
2025
Closest in time.
Z. Zhang, X. Li, L. Gao, Q. Liu, and J. Huang, “Tackling dual-resource flexible job shop scheduling problem in the production line reconfiguration scenario: An efficient meta-heuristic with critical path-based neighborhood search,” Adv. Eng. Inf. , vol. 65, p. 103282, 2025
2025
Closest in time.
Y. Li, X. Li, L. Gao, and Z. Lu, “Multi-agent deep reinforcement learning for dynamic reconfigurable shop scheduling considering batch processing and worker cooperation,” Rob. Comput. Integr. Manuf. , vol. 91, p. 102834, 2025
2025
Closest in time.
P. V. T. Dat, L. Doan, and H. T. T. Binh, “Hsevo: Elevating automatic heuristic design with diversity-driven harmony search and genetic algorithm using llms,” in AAAI Conf. Artif. Intell. , vol. 39, no. 25, 2025, pp. 26 931–26 938
2025
Closest in time.
H. Bao, Q. Pan, C.-M. Chew, L. Wang, and L. Gao, “An end-to-end framework for energy-efficient cascaded dual-shop collaborative scheduling with mating operations,” IEEE Trans. Cybern. , vol. 55, no. 10, pp. 4929–4942, 2025
2025
Closest in time.
Y. Wang, R. Wang, J. Sun, F. Deng, G. Wang, and J. Chen, “Attention enhanced reinforcement learning for flexible job shop scheduling with transportation constraints,” Expert Syst. Appl. , vol. 282, p. 127671, 2025
2025
Closest in time.
Y. Li, Q. Wang, X. Li, L. Gao, L. Fu, Y. Yu, and W. Zhou, “Real-time scheduling for flexible job shop with agvs using multiagent reinforcement learning and efficient action decoding,” IEEE Trans. Syst. Man Cybern.: Syst. , 2025
2025
Closest in time.
J. Huang, X. Li, Q. Liu, and L. Gao, “Efficient scheduling for fixed-type multi-robot collaborative problem in flexible job shop,” Rob. Comput. Integr. Manuf. , vol. 98, p. 103157, 2026
2026
Closest in time.