Annibale, P. (2019). An adaptive evolutionary algorithm based on non-Euclidean geometry for many-objective optimization. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO ‘19, 595–603.
Brandimarte, P. (1993). Routing and scheduling in a flexible job shop by taboo search. Annals of Operations Research, 41,157–183.
Brucker, P., & Schlie, R. (1990). Job-shop scheduling with multi-purpose machines. Computing, 45, 369–375.
Caldeira, R.H., Gnanavelbabu, A., & Vaidyanathan, T. (2020). An effective backtracking search algorithm for multi-objective flexible job shop scheduling considering new job arrivals and energy consumption. Computers & Industrial Engineering, 149, 106863.
Dauzere-Peres, S., & Paulli, J. (1997). An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search. Annals of Operations Research, 70, 281-306.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
Duan, J.G., & Wang, J.H. (2021). Energy-efficient scheduling for a flexible job shop with machine breakdowns considering machine idle time arrangement and machine speed level selection. Computers & Industrial Engineering, 161, 1-14.
Duan, J., & Wang, J. (2022). Robust scheduling for flexible machining job shop subject to machine breakdowns and new job arrivals considering system reusability and task recurrence. Expert Systems with Applications, 203, 117489.
He, L., Chiong, R., Li W., Dhakal, S., Cao, Y., & Zhang, Y. (2022). Multiobjective Optimization of Energy-Efficient JOB-Shop Scheduling With Dynamic Reference Point-Based Fuzzy Relative Entropy. IEEE Transactions on Industrial Informatics, 18(1), 600-610
Jiang, X.Y., Tian, Z.Q., Liu, W., & Li, Z. (2022). Energy-efficient scheduling of flexible job shops with complex processes: A case study for the aerospace industry complex components in China. Journal of Industrial Information Integration, 27, Article 100293.
Li, K., Zhang, X., Leung, J. Y. T., & Yang, S.L. (2016). Parallel Machine Scheduling Problems in Green Manufacturing Industry. Journal of Manufacturing Systems, 38, 98–106.
Li, X. Y., & Gao, L. (2016). An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem. International Journal of Production Economics, 174, 93–110.
Li, X., Peng, Z., Du, B., Guo, J., Xu, W., & Zhuang, K. (2017). Hybrid artificial bee colony algorithm with a rescheduling strategy for solving flexible job shop scheduling problems. Computers & Industrial Engineering, 113, 10–26.
Li, Y., He, Y., Wang, Y., Tao, F., & Sutherland J.W. (2020). An optimization method for energy-conscious production in flexible machining job shops with dynamic job arrivals and machine breakdowns. Journal of Cleaner Production, 254, 120009.
Li, Y., Gu, W., Yuan, M., & Tang, Y. (2022). Real-time data-driven dynamic scheduling for flexible job shop with insufficient transportation resources using hybrid deep Q network. Robotics and Computer-Integrated Manufacturing, 74, 102283.
Lu, C., Gao, L., Yi, J., & Li, X. (2020). Energy-efficient scheduling of distributed flow shop with heterogeneous factories: A real-world case from automobile industry in China. IEEE Transactions on Industrial Informatics, 17(10), 6687-6696.
Naimi, R., Nouiri, M., & Cardin, O. (2021). A Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives. Sustainability, 13(23), 13016.
Nicola, B., Boris, N., & Michael, E. (2007). SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research, 181(3), 1653–1669.
Nouiri, M., Bekrar, A., & Trentesaux, D. (2018). Towards energy efficient scheduling and rescheduling for dynamic flexible job shop problem. In: 16th IFAC symposium on information control problems in manufacturing INCOM 2018, vol. 51, no. 11. p. 1275–80.
Nouiri, M., Bekrar, A., & Trentesaux, D. (2020). An energy-efficient scheduling and rescheduling method for production and logistics systems. International Journal of Production Research, 58(11), 3263-3283.
Xin, X., Jiang, Q., Li, C, Li, S., & Chen, K. (2023). Permutation flow shop energy-efficient scheduling with a position-based learning effect. International Journal of Production Research, 61(2), 382-409
Xu, W., Shao, L., Yao, B., Zhou, Z., & Pham, D. T. (2016). Perception data-driven optimization of manufacturing equipment service scheduling in sustainable manufacturing. Journal of Manufacturing Systems, 41, 86-101.
Van, V. D. A., & Lamont, G. B., (2000). On measuring multi-objective evolutionary algorithm performance. in Proc. 2000 Congress on Evolutionary Computation, La Jolla, USA, 2000, 204–211.doi:10.1109/CEC.2000.870296.
Van, V. D. A., & Lamont, G. B. (1998). Evolutionary computation and convergence to a Pareto front. in Proc. Late-breaking Paper at the Genetic Programming 1998 Conference.
Wang, K., Wu, M., Sun, Y., Shi, X., Sun, A., & Zhang, P. (2019). Resource abundance, industrial structure, and regional carbon emissions efficiency in China. Resources Policy, 60, 203-214.
Wang, Y., Che, A., & Feng, J. (2023). Energy-efficient unrelated parallel machine scheduling with general position-based deterioration, International Journal of Production Research, 61(17), 5886-5900.
Wang, Y.J., Wang, G.-G., Tian, F.-M., Gong, D.-W., & Pedrycz, W. (2023). Solving energy-efficient fuzzy hybrid flow-shop scheduling problem at a variable machine speed using an extended NSGA-II. Engineering Applications in Artificial Intelligence, 121, Article 105977.
Zhao, F., Jiang, T., & Wang, L. (2022). A reinforcement learning driven cooperative meta-heuristic algorithm for energy-efficient distributed no-wait flow-shop scheduling with sequence-dependent setup time. IEEE Transactions on Industrial Informatics, 19(7), 8427-8440.
Zitzer, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. In: Proceedings of the EROGEN Conference, pp. 182–197.
Zitzler, E., Brockhoff, D., & Thiele, L. (2007). The hypervolume indicator revisited: on the design of Pareto-compliant indicators via weighted integration. Evolutionary multi-criterion optimization. Springer, pp. 862–876
Zhu, Z., & Zhou, X. (2020). An efficient evolutionary grey wolf optimizer for multi-objective flexible job shop scheduling problem with hierarchical job precedence constraints. Computers and Industrial Engineering, 140(January), Article 106280.