How to cite this paper
Ojstersek, R., Brezocnik, M & Buchmeister, B. (2020). Multi-objective optimization of production scheduling with evolutionary computation: A review.International Journal of Industrial Engineering Computations , 11(3), 359-376.
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Frutos, M., Tohme, F., Delbianco, F. & Miguel, F. (2016). An alternative hybrid evolutionary technique focused on allocating machines and sequencing operations. International Journal of Industrial Engineering Computations, 7(4), 585–596.
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Hao, X., Gen, M., Lin, L. & Suer, G. A. (2017). Effective multiobjective EDA for bi-criteria stochastic job-shop scheduling problem. Journal of Intelligent Manufacturing, 28(3), 833–845.
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Becker, C. & Scholl, A. (2009). Balancing assembly lines with variable parallel workplaces: Problem definition and effective solution procedure. European Journal of Operational Research, 199(2), 359–374.
Branke, J., Branke J., Deb, K., Miettinen, K. & Slowinski, R. (2008). Multiobjective optimization: Interactive and evolutionary approaches. New York: Springer Science & Business Media.
Buchmeister, B. & Palcic, I. (2015). Advanced Job Shop Scheduling Methods. Vienna: DAAAM International.
Centobelli, P., Cerchione, R., Murino, T. & Gallo, M. (2016). Layout and material flow optimization in digital factory. International Journal of Simulation Modelling, 15(2), 223–235.
Chang, P.-T., Lin, K.-P., Pai, P.-F., Zhonf, C.-Z., Lin, C.-H. & Hung, L.-T. (2008). Ant colony optimization system for a multi-quantitative and qualitative objective job-shop parallel-machine-scheduling problem. International Journal of Production Research, 46(20), 5719–5759.
Chang, P. C., Chen, S. H., Zhang, Q. & Lin, J. L. (2008). MOEA/D for flowshop scheduling problems. In 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). IEEE, Hong Kong, 1433–1438.
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In International Conference on Parallel Problem Solving From Nature, Springer, Berlin, 849–858.
Deb, K. & Jain, H. (2014). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577–601.
Dehghanimohammadabadi, M. & Keyser, T. K. (2017). Intelligent simulation: integration of SIMIO and MATLAB to deploy decision support systems to simulation environment. Simulation Modelling Practice and Theory, 71, 45–60.
Du, J. & Leung, J. Y.-T. (1990). Minimizing total tardiness on one machine is NP-hard. Mathematics of operations research, 15(3), 483–495.
Duhamel, C., Lacomme, P., Quilliot, A. & Toussaint, H. (2011). A multi-start evolutionary local search for the two-dimensional loading capacitated vehicle routing problem. Computers & Operations Research, 38(3), 617–640.
Eberhart, R. & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Micro Machine and Human Science, MHS’95, IEEE, Nagoya, 39–43.
Elloumi, S. & Fortemps, P. (2010). A hybrid rank-based evolutionary algorithm applied to multi-mode resource-constrained project scheduling problem. European Journal of Operational Research, 205(1), 31–41.
Esquivel, S., Ferrero, S., Gallard, R., Salto, C., Alfonso, H. & Schutz, M. (2002). Enhanced evolutionary algorithms for single and multiobjective optimization in the job shop scheduling problem. Knowledge-Based Systems, 15(1–2), 13–25.
Fishman, G. S. (2013). Discrete-event simulation: modeling, programming, and analysis. New York: Springer Science & Business Media.
Frutos, M., Tohme, F., Delbianco, F. & Miguel, F. (2016). An alternative hybrid evolutionary technique focused on allocating machines and sequencing operations. International Journal of Industrial Engineering Computations, 7(4), 585–596.
Gen, M., Lin, L. & Zhang, W. (2015). Multiobjective hybrid genetic algorithms for manufacturing scheduling: Part I models and algorithms. In Proceedings of the Ninth International Conference on Management Science and Engineering Management. Springer, Berlin, 3–25.
Glover, F.W. & Kochenberger, G. A. (2006) Handbook of metaheuristics. New York: Springer Science & Business Media.
Graham, R. L., Lawler, E. L., Lenstra, J. K. & Kan, A. H. G. R. (1979). Optimization and approximation in deterministic sequencing and scheduling: a survey. Annals of discrete mathematics, 1979, 287–326.
Granja, C., Almada-Lobo, B., Janela, F., Seabra, J. & Mendes, A. (2014). An optimization based on simulation approach to the patient admission scheduling problem using a linear programing algorithm. Journal of Biomedical Informatics, 52, 427–437.
Hao, X., Gen, M., Lin, L. & Suer, G. A. (2017). Effective multiobjective EDA for bi-criteria stochastic job-shop scheduling problem. Journal of Intelligent Manufacturing, 28(3), 833–845.
Hinderer, K., Rieder, U. & Stieglitz, M. (2016). Dynamic optimization. New York: Springer.
Holland, J. H. & Goldberg, D. (1989). Genetic algorithms in search, optimization and machine learning. Boston: Addison-Wesley.
Huang, J. & Süer, G. A. (2015). A dispatching rule-based genetic algorithm for multi-objective job shop scheduling using fuzzy satisfaction levels. Computers & Industrial Engineering, 86, 29–42.
Hultmann-Ayala, H. V., dos Santos-Coelho, L. and Reynoso-Meza, G. (2017). Heuristic Kalman Algorithm for Multiobjective Optimization. IFAC-PapersOnLine, 50(1), 4460–4465.
Ishibuchi, H. & Murata, T. (1998). A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 28(3), 392–403.
Jia, S. & Hu, Z.-H. (2014). Path-relinking Tabu search for the multi-objective flexible job shop scheduling problem. Computers & Operations Research, 47, 11–26.
Joines, J. A. & Roberts, S. D. (2013). Simulation modeling with SIMIO: a workbook. Sewickley: Simio LLC.
Kacem, I., Hammadi, S. & Borne, P. (2002). Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic. Mathematics and Computers in Simulation, 60(3–5), 245–276.
Klancnik, S., Hrelja, M., Balic, J. & Brezocnik, M. (2016). Multi-objective optimization of the turning process using a Gravitational Search Algorithm and NSGA-II approach. Advances in Production Engineering & Management, 11(4), 366–376.
Konak, A., Coit, D. W. & Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety, 91(9), 992–1007.
Kramer, O. (2017) Genetic algorithm essentials. New York: Springer.
Kundaki, N. & Kulak, O. (2016). Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem. Computers & Industrial Engineering, 96, 31–51.
Law, A. M., Kelton, W. D. & Kelton, W. D. (2007). Simulation modeling and analysis. New York: McGraw-Hill.
Lee, S. M. & Asllani, A. A. (2004). Job scheduling with dual criteria and sequence-dependent setups: mathematical versus genetic programming. Omega, 32(2), 145–153.
Li, J.-Q., Pan, Q.-K. & Gao, K.-Z. (2011). Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. The International Journal of Advanced Manufacturing Technology, 55(9–12), 1159–1169.
Li, J., Pan, Q. & Duan, P. (2016). An improved artificial bee colony algorithm for solving hybrid flexible flowshop with dynamic operation skipping. IEEE Transactions on Cybernetics, 46(6), 1311–1324.
Li, J., Pan, Q. & Liang, Y.-C. (2010). An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering, 59(4), 647–662.
Li, Y., Yao, X. & Zhou, J. (2016). Multi-objective optimization of cloud manufacturing service composition with cloud-entropy enhanced genetic algorithm. Journal of Mechanical Engineering, 62(10), 577–590.
Lin, L. & Gen, M. (2018). Hybrid evolutionary optimisation with learning for production scheduling: state-of-the-art survey on algorithms and applications. International Journal of Production Research, 56(1–2), 193–223.
Lin, Z. & Wang, C. (2013). Scheduling parallel Kalman filters for multiple processes. Automatica, 9(1), 9–16.
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