A Shuffled Frog Leaping Algorithm with Q-Learning for Distributed Hybrid Flow Shop Scheduling Problem with Energy-Saving

[1] J. C. Cai and D. M. Lei. A cooperated shuffled frog-leaping algorithm for distributed energy-efficient hybrid flow shop scheduling with fuzzy processing time. Complex & Intelligent Systems, 7(5):2235–2253, 2021.Search in Google Scholar

[2] J. C. Cai, R. Zhou, and D. M. Lei. Dynamic shuffled frog-leaping algorithm for distributed hybrid flow shop scheduling with multiprocessor tasks. Engineering Applications of Artificial Intelligence, 90:103540, 2020.Search in Google Scholar

[3] J. C. Cai, R. Zhou, and D. M. Lei. Fuzzy distributed two-stage hybrid flow shop scheduling problem with setup time: collaborative variable search. Journal of Intelligent & Fuzzy Systems, 38(3):3189–3199, 2020.Search in Google Scholar

[4] J.C. Cai, D.M Lei, and M. Li. A shuffled frog-leaping algorithm with memeplex quality for bi-objective distributed scheduling in hybrid flow shop. International Journal of Production Research, 59(18):5404–5421, 2020.Search in Google Scholar

[5] J.C. Cai, D.M Lei, J. Wang, and L. Wang. A novel shuffled frog-leaping algorithm with reinforcement learning for distributed assembly hybrid flow shop scheduling. International Journal of Production Research, 61(4):1233-1251, 2023.Search in Google Scholar

[6] R.H. Chen, B. Yang, S. Li, and S.l. Wang. A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem. Computers & Industrial Engineering, 149:106778, 2020.Search in Google Scholar

[7] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation, 6(2):182–197, 2002.Search in Google Scholar

[8] Yu Du, Jun-qing Li, Chao Luo, and Lei-lei Meng. A hybrid estimation of distribution algorithm for distributed flexible job shop scheduling with crane transportations. Swarm and Evolutionary Computation, 62, 2021.Search in Google Scholar

[9] M. Eusuff, K. Lansey, and F. Pasha. Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Engineering Optimization, 38(2):129–154, 2006.Search in Google Scholar

[10] J. H. Hao, J. Q. Li, Y. Du, M. X. Song, P. Duan, and Y. Y. Zhang. Solving distributed hybrid flowshop scheduling problems by a hybrid brain storm optimization algorithm. IEEE Access, 7:66879–66894, 2019.Search in Google Scholar

[11] E. D. Jiang, L. Wang, and Z. P. Peng. Solving energy-efficient distributed job shop scheduling via multi-objective evolutionary algorithm with decomposition. Swarm and Evolutionary Computation, 58, 2020.Search in Google Scholar

[12] E. D. Jiang, L. Wang, and J. J. Wang. Decomposition-based multi-objective optimization for energy-aware distributed hybrid flow shop scheduling with multiprocessor tasks. Tsinghua Science and Technology, 26(5):646–663, 2021.Search in Google Scholar

[13] D. M. Lei, L. Gao, and Y. L. Zheng. A novel teaching-learning-based optimization algorithm for energy-efficient scheduling in hybrid flow shop. Ieee Transactions on Engineering Management, 65(2):330–340, 2018.Search in Google Scholar

[14] D. Lei and T. Wang. Solving distributed two-stage hybrid flowshop scheduling using a shuffled frog-leaping algorithm with memeplex grouping. Engineering Optimization, 52(9):1461–1474, 2019.Search in Google Scholar

[15] J. Q. Li, J. K. Li, L. J. Zhang, H. Y. Sang, Y. Y. Han, and Q. D. Chen. Solving type-2 fuzzy distributed hybrid flowshop scheduling using an improved brain storm optimization algorithm. International Journal of Fuzzy Systems, 23(4):1194–1212, 2021.Search in Google Scholar

[16] Y. L. Li, X. Y. Li, L. Gao, and L. L. Meng. An improved artificial bee colony algorithm for distributed heterogeneous hybrid flowshop scheduling problem with sequence-dependent setup times. Computers & Industrial Engineering, 147, 2020.Search in Google Scholar

[17] Y.L. Li, F. Li, Q.K. Pan, L. Gao, and M. F. Tasgetiren. An artificial bee colony algorithm for the distributed hybrid flowshop scheduling problem. Procedia Manufacturing, 39:1158–1166, 2019.Search in Google Scholar

[18] Y.L. Li, X.Y. Li, L. Gao, B. Zhang, Q.K. Pan, M. F. Tasgetiren, and Leilei Meng. A discrete artificial bee colony algorithm for distributed hybrid flow-shop scheduling problem with sequence-dependent setup times. International Journal of Production Research, 59(13):3880–3899, 2021.Search in Google Scholar

[19] C. Lu, L. Gao, Q. K. Pan, X. Y. Li, and J. Zheng. A multi-objective cellular grey wolf optimizer for hybrid flowshop scheduling problem considering noise pollution. Applied Soft Computing, 75:728–749, 2019.Search in Google Scholar

[20] L. Meng, K. Gao, Y. Ren, B. Zhang, H. Sang, and C. Zhang. Novel milp and cp models for distributed hybrid flowshop scheduling problem with sequence-dependent setup times. Swarm and Evolutionary Computation, 71:101058, 2022.Search in Google Scholar

[21] L. Meng, Y.g Ren, B. Zhang, J. Li, H. Sang, and C. Zhang. Milp modeling and optimization of energy-efficient distributed flexible job shop scheduling problem. Ieee Access, 8:191191–191203, 2020.Search in Google Scholar

[22] Z. Pan, D. Lei, and L. Wang. A knowledge-based two-population optimization algorithm for distributed energy-efficient parallel machines scheduling. IEEE Trans Cybern, PP, 2020.Search in Google Scholar

[23] H. Qin, T. Li, Y. Teng, and K. Wang. Integrated production and distribution scheduling in distributed hybrid flow shops. Memetic Computing, 13(2):185–202, 2021.Search in Google Scholar

[24] H.X. Qin, Y.Y. Han, B. Zhang, L.L. Meng, Y.P. Liu, Q.K. Pan, and D.W. Gong. An improved iterated greedy algorithm for the energy-efficient blocking hybrid flow shop scheduling problem. Swarm and Evolutionary Computation, 69, 2022.Search in Google Scholar

[25] W. S. Shao, Z. S. Shao, and D. C. Pi. Multi-objective evolutionary algorithm based on multiple neighborhoods local search for multi-objective distributed hybrid flow shop scheduling problem. Expert Systems with Applications, 183, 2021.Search in Google Scholar

[26] G. Wang, X. Li, L. Gao, and P. Li. An effective multi-objective whale swarm algorithm for energy-efficient scheduling of distributed welding flow shop. Annals of Operations Research, page in press, 2021.Search in Google Scholar

[27] J. Wang, D. Lei, and J. Cai. An adaptive artificial bee colony with reinforcement learning for distributed three-stage assembly scheduling with maintenance. Applied Soft Computing, page 108371, 2021.Search in Google Scholar

[28] J.J. Wang and L. Wang. A bi-population cooperative memetic algorithm for distributed hybrid flow-shop scheduling. IEEE Transactions on Emerging Topics in Computational Intelligence, 5(6):947–961, 2020.Search in Google Scholar

[29] J.J. Wang and L. Wang. A cooperative memetic algorithm with learning-based agent for energy-aware distributed hybrid flow-shop scheduling. IEEE Transactions on Evolutionary Computation, 2021.Search in Google Scholar

[30] L. Wang and D. D. Li. Fuzzy distributed hybrid flow shop scheduling problem with heterogeneous factory and unrelated parallel machine: a shuffled frog leaping algorithm with collaboration of multiple search strategies. IEEE Access, 8:214209–214223, 2020.Search in Google Scholar

[31] K. C. Ying and S. W. Lin. Minimizing makespan for the distributed hybrid flowshop scheduling problem with multiprocessor tasks. Expert Systems with Applications, 92:132–141, 2018.Search in Google Scholar

[32] B. Zhang, Q. K. Pan, L. Gao, X. Y. Li, L. L. Meng, and K. K. Peng. A multiobjective evolutionary algorithm based on decomposition for hybrid flow-shop green scheduling problem. Computers & Industrial Engineering, 136:325–344, 2019.Search in Google Scholar

[33] J. Zheng, L. Wang, and J. J. Wang. A cooperative coevolution algorithm for multi-objective fuzzy distributed hybrid flow shop. Knowledge-Based Systems, 194:105536, 2020.Search in Google Scholar