Enhanced NSGA-II for multi-objective energy-saving flexible job shop scheduling

Ding, 2016, Carbon-efficient scheduling of flow shops by multi-objective optimization, Eur. J. Oper. Res., 248, 758, 10.1016/j.ejor.2015.05.019 Mansouri, 2016, Green scheduling of a two-machine flowshop: trade-off between makespan and energy consumption, Eur. J. Oper. Res., 248, 772, 10.1016/j.ejor.2015.08.064 Luo, 2013, Hybrid flow shop scheduling considering machine electricity consumption cost, Int. J. Prod. Econ., 146, 423, 10.1016/j.ijpe.2013.01.028 Zhang, 2016, Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption, J. Clean. Prod., 112, 3361, 10.1016/j.jclepro.2015.09.097 Li, 2018, Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions, J. Clean. Prod., 181, 584, 10.1016/j.jclepro.2018.02.004 Salido, 2016, A genetic algorithm for energy-efficiency in job-shop scheduling, Int. J. Adv. Manuf. Technol., 85, 1303, 10.1007/s00170-015-7987-0 Mokhtari, 2017, An energy-efficient multi-objective optimization for flexible job-shop scheduling problem, Comput. Chem. Eng., 104, 339, 10.1016/j.compchemeng.2017.05.004 Liu, 2020, Green scheduling of flexible job shops based on NSGA-Ⅱ under TOU power price, CHINA Mech. Eng., 31, 576 Zhong, 2018, HCS algorithm for multi-objective flow shop scheduling problems with energy consumption, CHINA Mech. Eng., 29, 2674 Wang, 2018, Multi-objective parallel variable neighborhood search for energy consumption scheduling inblocking fowshops, IEEE Access, 6, 68686, 10.1109/ACCESS.2018.2879600 Lin, 2015, A multi-objective teaching-learning-based optimization algorithm to scheduling in turning processes for minimizing makespan and carbon footprint, J. Clean. Prod., 101, 337, 10.1016/j.jclepro.2015.03.099 Jiang, 2018, Energy-efficient scheduling for a job shop using grey wolf optimization algorithm with double-searching mode, Math. Probl. Eng., 2018, 1 Kawaguchi, 2018, Improved parallel reactive tabu search based job-shop scheduling considering minimization ofsecondary energy costs in factories, Soc. Instrum. Control Eng. Jpn., 765 Escamilla, 2016, A metaheuristic technique for energy-efficiency in job-shop scheduling, Knowl. Eng. Rev., 31, 475, 10.1017/S026988891600031X Jiang, 2018, Energy-efficient scheduling for a job shop using an improved whale optimization algorithm, Mathematics, 6, 1, 10.3390/math6110220 Yin, 2017, A novel mathematical model and multi-objective method for the low-carbon flexible job shop scheduling problem, Sustain. Comput. Inform. Syst., 13, 15 Piroozfard, 2018, Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm, Resour. Conserv. Recycl., 128, 267, 10.1016/j.resconrec.2016.12.001 Lei, 2017, A shuffled frog-leaping algorithm for flexible job shop scheduling with the consideration of energy consumption, Int. J. Prod. Res., 55, 3126, 10.1080/00207543.2016.1262082 Jiang, 2018, Optimizing the low-carbon flexible job shop scheduling problem considering energy consumption, IEEE Access, 6, 46346, 10.1109/ACCESS.2018.2866133 Wang, 2018, A two-stage optimization method for energy-saving flexible job-shop scheduling based on energy dynamic characterization, J. Clean. Prod., 188, 575, 10.1016/j.jclepro.2018.03.254 He, 2022, An improved african vulture optimization algorithm for dual-resource constrained multi-objective flexible job shop scheduling problems, Sensors, 23, 1, 10.3390/s23010090 Yang, 2022, A novel hybrid whale optimization algorithm for flexible job-shop scheduling problem, Machines, 10, 1, 10.3390/machines10080618 Yang, 2022, Hybrid memetic algorithm to solve multiobjective distributed fuzzy flexible job shop scheduling problem with transfer, Processes, 10, 1, 10.3390/pr10081517 Tan, 2021, A fatigue-conscious dual resource constrained flexible job shop scheduling problem by enhanced NSGA-II: an application from casting workshop, Comput. Ind. Eng., 160, 1, 10.1016/j.cie.2021.107557 Abualigah, 2021, Aquila optimizer: a novel meta-heuristic optimization algorithm, Comput. Ind. Eng., 157, 1, 10.1016/j.cie.2021.107250 Abualigah, 2022, Reptile Search Algorithm (RSA): a nature-inspired meta-heuristic optimizer, Expert Syst. Appl., 191, 1, 10.1016/j.eswa.2021.116158 Abualigah, 2021, The arithmetic optimization algorithm, Comput. Methods Appl. Mech. Eng., 376, 1, 10.1016/j.cma.2020.113609 Abualigah, 2021, Applications, deployments, and integration of Internet of Drones (IoD): a review, IEEE Sens. J., 21, 25532, 10.1109/JSEN.2021.3114266 Deb, 2002, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., 6, 182, 10.1109/4235.996017 Zhang, 2010, Improved NSGA-II for the multi-objective flexible job-shop scheduling problem, J. Mech. Eng., 46, 156 Babazadeh, 2018, An enhanced NSGA-II algorithm for fuzzy bi-objective assembly line balancing problems, Comput. Ind. Eng., 123, 189, 10.1016/j.cie.2018.06.014 Wang, 2017, based memetic algorithm for multi-objective parallel flowshop scheduling problem, Comput. Ind. Eng., 113, 185, 10.1016/j.cie.2017.09.009 Han, 2018, Improved NSGA-II algorithm for multi-objective scheduling problem in hybrid flow shop, Proc. 2017 9th Int. Conf. Model. Identif. Control. ICMIC 2017, 2018-March, 740 Wang, 2019, A NSGA-II algorithm hybridizing local simulated- annealing operators for a bi-criteria robust job-shop scheduling problem under scenarios, IEEE Trans. Fuzzy Syst., 27, 1075, 10.1109/TFUZZ.2018.2879789 Li, 2018, A local search strategy based on density for NSGA-II algorithm, Control Decis., 33, 60 Ahmadi, 2016, Memory-based adaptive partitioning (MAP) of search space for the enhancement of convergence in Pareto-based multi-objective evolutionary algorithms, Appl. Soft Comput. J., 41, 400, 10.1016/j.asoc.2016.01.029 Tay, 2008, Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems, Comput. Ind. Eng., 54, 453, 10.1016/j.cie.2007.08.008 Brandimarte, 1993, Routing and scheduling in a flexible job shop by tabu search, Ann. Oper. Res., 41, 157, 10.1007/BF02023073 Le, 2013, Classification of energy consumption patterns for energy audit and machine scheduling in industrial manufacturing systems, Trans. Inst. Meas. Control., 35, 583, 10.1177/0142331212460883 Baker, 1984, Sequencing rules and due-date assignments in a job shop, Manag. Sci., 30, 1093, 10.1287/mnsc.30.9.1093 Luan, 2019, Optimizing the low-carbon flexible job shop scheduling problem with discrete whale optimization algorithm, Mathematics, 7, 1 Liu, 2018, Job shop scheduling with a combination of four buffering constraints, Int. J. Prod. Res., 56, 3274, 10.1080/00207543.2017.1401240 Liu, 2016, Parallel-identical-machine job-shop scheduling with different stage-dependent buffering requirements, Comput. Oper. Res., 74, 31, 10.1016/j.cor.2016.04.023 Liu, 2005, Metaheuristics for minimizing the makespan of the dynamic shop scheduling problem, Adv. Eng. Softw., 36, 199, 10.1016/j.advengsoft.2004.10.002