How to cite this paper
Rodríguez-Espinosa, D., Cruz-Vargas, D., Delgado-Merchán, D., Gonzalez-Estupiñán, D & González-Neir, E. (2024). NSGA-II simheuristic to solve a multi-objective flexible flow shop problem under stochastic machine breakdowns.Journal of Project Management, 9(4), 493-512.
Refrences
Abu-Marrul, V., Martinelli, R., Hamacher, S., & Gribkovskaia, I. (2023). Simheuristic algorithm for a stochastic paral-lel machine scheduling problem with periodic re-planning assessment. Annals of Operations Research, 320(2), 547–572. https://doi.org/10.1007/s10479-022-04534-5
Ahmadi, E., Zandieh, M., Farrokh, M., & Emami, S. M. (2016). A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms. Computers & Operations Research, 73, 56–66. https://doi.org/10.1016/j.cor.2016.03.009
Allahverdi, A., Aydilek, A., & Aydilek, H. (2016). Minimizing the number of tardy jobs on a two-stage assembly flow-shop. Journal of Industrial and Production Engineering, 33(6), 391–403. https://doi.org/10.1080/21681015.2016.1151466
Almeder, C., & Hartl, R. F. (2013). A metaheuristic optimization approach for a real-world stochastic flexible flow shop problem with limited buffer. International Journal of Production Economics, 145(1), 88–95. https://doi.org/10.1016/j.ijpe.2012.09.014
Al-Turki, U. M., Saleh, H., Deyab, T., & Almoghathawi, Y. (2012). Resource Allocation and Job Dispatching for Unre-liable Flexible Flow Shop Manufacturing System. Advanced Materials Research, 445, 947–952. https://doi.org/10.4028/www.scientific.net/AMR.445.947
Azadeh, A., Goodarzi, A. H., Kolaee, M. H., & Jebreili, S. (2018). An efficient simulation–neural network–genetic algo-rithm for flexible flow shops with sequence-dependent setup times, job deterioration and learning effects. Neural Computing and Applications, 6, 1–15. https://doi.org/10.1007/s00521-018-3368-6
Behnamian, J., Fatemi Ghomi, S. M. T., & Zandieh, M. (2009). A multi-phase covering Pareto-optimal front method to multi-objective scheduling in a realistic hybrid flowshop using a hybrid metaheuristic. Expert Systems with Applica-tions, 36(8), 11057–11069. https://doi.org/10.1016/j.eswa.2009.02.080
Brunner, E., Dette, H., & Munk, A. (1997). Box-Type Approximations in Nonparametric Factorial Designs. Journal of the American Statistical Association, 92(440), 1494–1502. https://doi.org/10.1080/01621459.1997.10473671
Caldeira, R. H., & Gnanavelbabu, A. (2021). A simheuristic approach for the flexible job shop scheduling problem with stochastic processing times. SIMULATION, 97(3), 215–236. https://doi.org/10.1177/0037549720968891
Chen, C.-L., & Chen, C.-L. (2009). Bottleneck-based heuristics to minimize total tardiness for the flexible flow line with unrelated parallel machines. Computers & Industrial Engineering, 56(4), 1393–1401. https://doi.org/10.1016/j.cie.2008.08.016
Choi, S. H., & Wang, K. (2012). Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach. Computers & Industrial Engineering, 63(2), 362–373. https://doi.org/10.1016/j.cie.2012.04.001
Das, K. (2008). A comparative study of exponential distribution vs Weibull distribution in machine reliability analysis in a CMS design. Computers & Industrial Engineering, 54(1), 12–33. https://doi.org/10.1016/j.cie.2007.06.030
de León, A. D., Lalla-Ruiz, E., Melián-Batista, B., & Moreno-Vega, J. M. (2021). A simulation–optimization framework for enhancing robustness in bulk berth scheduling. Engineering Applications of Artificial Intelligence, 103, 104276. https://doi.org/10.1016/j.engappai.2021.104276
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II. In CEUR Workshop Proceedings (Vol. 1133, pp. 849–858). https://doi.org/10.1007/3-540-45356-3_83
Ebrahimi, M., Fatemi Ghomi, S. M. T., & Karimi, B. (2014). Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates. Applied Mathematical Modelling, 38(9–10), 2490–2504. https://doi.org/10.1016/j.apm.2013.10.061
Fu, Y., Zhou, M., Guo, X., & Qi, L. (2020). Scheduling Dual-Objective Stochastic Hybrid Flow Shop With Deteriorating Jobs via Bi-Population Evolutionary Algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(12), 5037–5048. https://doi.org/10.1109/TSMC.2019.2907575
Gonzalez-Neira, E. M., Ferone, D., Hatami, S., & Juan, A. A. (2017). A biased-randomized simheuristic for the distrib-uted assembly permutation flowshop problem with stochastic processing times. Simulation Modelling Practice and Theory, 79, 23–36. https://doi.org/10.1016/j.simpat.2017.09.001
González-Neira, E. M., García-Cáceres, R. G., Caballero-Villalobos, J. P., Molina-Sánchez, L. P., & Montoya-Torres, J. R. (2016). Stochastic flexible flow shop scheduling problem under quantitative and qualitative decision criteria. Computers & Industrial Engineering, 101, 128–144. https://doi.org/10.1016/j.cie.2016.08.026
González-Neira, E. M., Montoya-Torres, J. R., & Barrera, D. (2017). Flow-shop scheduling problem under uncertainties: Review and trends. International Journal of Industrial Engineering Computations, 8(4), 399–426. https://doi.org/10.5267/j.ijiec.2017.2.001
Han, W., Deng, Q., Gong, G., Zhang, L., & Luo, Q. (2021). Multi-objective evolutionary algorithms with heuristic de-coding for hybrid flow shop scheduling problem with worker constraint. Expert Systems with Applications, 168, 114282. https://doi.org/10.1016/j.eswa.2020.114282
Holthaus, O. (1999). Scheduling in job shops with machine breakdowns: An experimental study. Computers and Indus-trial Engineering, 36(1), 137–162. https://doi.org/10.1016/S0360-8352(99)00006-6
Hsieh, J.-C., Chang, P.-C., & Hsu, L.-C. (2003). Scheduling of drilling operations in printed circuit board factory☆. Computers & Industrial Engineering, 44(3), 461–473. https://doi.org/10.1016/S0360-8352(02)00231-0
Huang, Y., Deng, L., Wang, J., Qiu, W., & Liu, J. (2023). Modeling and solution for hybrid flow-shop scheduling prob-lem by two-stage stochastic programming. Expert Systems with Applications, 233. https://doi.org/10.1016/j.eswa.2023.120846
Ji, M., Yang, Y., Duan, W., Wang, S., & Liu, B. (2016). Scheduling of no-wait stochastic distributed assembly flowshop by hybrid PSO. 2016 IEEE Congress on Evolutionary Computation (CEC), 2649–2654. https://doi.org/10.1109/CEC.2016.7744120
Jiang, S., Liu, M., Hao, J., & Qian, W. (2015). A bi-layer optimization approach for a hybrid flow shop scheduling prob-lem involving controllable processing times in the steelmaking industry. Computers & Industrial Engineering, 87, 518–531. https://doi.org/10.1016/j.cie.2015.06.002
Juan, A. A., Barrios, B. B., Vallada, E., Riera, D., & Jorba, J. (2014). A simheuristic algorithm for solving the permuta-tion flow shop problem with stochastic processing times. Simulation Modelling Practice and Theory, 46, 101–117. https://doi.org/10.1016/j.simpat.2014.02.005
Juan, A. A., Faulin, J., Grasman, S. E., Rabe, M., & Figueira, G. (2015). A review of simheuristics: Extending metaheu-ristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2, 62–72. https://doi.org/10.1016/j.orp.2015.03.001
Kianfar, K., Fatemi Ghomi, S. M. T., & Oroojlooy Jadid, A. (2012). Study of stochastic sequence-dependent flexible flow shop via developing a dispatching rule and a hybrid GA. Engineering Applications of Artificial Intelligence, 25(3), 494–506. https://doi.org/10.1016/j.engappai.2011.12.004
Kim, D.-W., Na, D.-G., & Frank Chen, F. (2003). Unrelated parallel machine scheduling with setup times and a total weighted tardiness objective. Robotics and Computer-Integrated Manufacturing, 19(1–2), 173–181. https://doi.org/10.1016/S0736-5845(02)00077-7
Lin, J. T., & Chen, C.-M. (2015). Simulation optimization approach for hybrid flow shop scheduling problem in semi-conductor back-end manufacturing. Simulation Modelling Practice and Theory, 51, 100–114. https://doi.org/10.1016/j.simpat.2014.10.008
Lin, J. T., Chen, C.-M., Chiu, C.-C., & Fang, H.-Y. (2013). Simulation optimization with PSO and OCBA for semicon-ductor back-end assembly. Journal of Industrial and Production Engineering, 30(7), 452–460. https://doi.org/10.1080/21681015.2013.860926
Lin, Y.-K., & Huang, D.-H. (2020). Reliability analysis for a hybrid flow shop with due date consideration. Reliability Engineering & System Safety, 199, 105905. https://doi.org/10.1016/j.ress.2017.07.008
Liu, Q., Ullah, S., & Zhang, C. (2011). An improved genetic algorithm for robust permutation flowshop scheduling. The International Journal of Advanced Manufacturing Technology, 56(1–4), 345–354. https://doi.org/10.1007/s00170-010-3149-6
Liu, Y., Shen, W., Zhang, C., & Sun, X. (2023). Agent-based simulation and optimization of hybrid flow shop consider-ing multi-skilled workers and fatigue factors. Robotics and Computer-Integrated Manufacturing, 80. https://doi.org/10.1016/j.rcim.2022.102478
Minella, G., Ruiz, R., & Ciavotta, M. (2011). Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems. Computers & Operations Research, 38(11), 1521–1533. https://doi.org/10.1016/j.cor.2011.01.010
Mirabi, M., Ghomi, S. M. T. F., & Jolai, F. (2013). A two-stage hybrid flowshop scheduling problem in machine break-down condition. Journal of Intelligent Manufacturing, 24(1), 193–199. https://doi.org/10.1007/s10845-011-0553-1
Pinedo, M. L. (2012). Scheduling: Theory, algorithms and systems. In Springer (4th ed., Vol. 4). Springer Science & Business Media. https://doi.org/10.1007/978-1-4614-2361-4
Qin, W., Zhang, J., & Song, D. (2018). An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time. Journal of Intelligent Manufacturing, 29(4), 891–904. https://doi.org/10.1007/s10845-015-1144-3
Rahmani, D., Heydari, M., Makui, A., & Zandieh, M. (2013). A new approach to reducing the effects of stochastic dis-ruptions in flexible flow shop problems with stability and nervousness. International Journal of Management Sci-ence and Engineering Management, 8(3), 173–178. https://doi.org/10.1080/17509653.2013.812332
Rajendran, C., & Chaudhuri, D. (1992). Scheduling in n-job, m-stage flowshop with parallel processors to minimize makespan. International Journal of Production Economics, 27(2), 137–143. https://doi.org/10.1016/0925-5273(92)90006-S
Rodríguez-Espinosa, C. A., González-Neira, E. M., & Zambrano-Rey, G. M. (2023). A simheuristic approach using the NSGA-II to solve a bi-objective stochastic flexible job shop problem. Journal of Simulation, 1–25. https://doi.org/10.1080/17477778.2023.2231877
Rooeinfar, R., Raissi, S., & Ghezavati, V. (2019). Stochastic flexible flow shop scheduling problem with limited buffers and fixed interval preventive maintenance: a hybrid approach of simulation and metaheuristic algorithms. SIMULA-TION, 95(6), 509–528. https://doi.org/10.1177/0037549718809542
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Allahverdi, A., Aydilek, A., & Aydilek, H. (2016). Minimizing the number of tardy jobs on a two-stage assembly flow-shop. Journal of Industrial and Production Engineering, 33(6), 391–403. https://doi.org/10.1080/21681015.2016.1151466
Almeder, C., & Hartl, R. F. (2013). A metaheuristic optimization approach for a real-world stochastic flexible flow shop problem with limited buffer. International Journal of Production Economics, 145(1), 88–95. https://doi.org/10.1016/j.ijpe.2012.09.014
Al-Turki, U. M., Saleh, H., Deyab, T., & Almoghathawi, Y. (2012). Resource Allocation and Job Dispatching for Unre-liable Flexible Flow Shop Manufacturing System. Advanced Materials Research, 445, 947–952. https://doi.org/10.4028/www.scientific.net/AMR.445.947
Azadeh, A., Goodarzi, A. H., Kolaee, M. H., & Jebreili, S. (2018). An efficient simulation–neural network–genetic algo-rithm for flexible flow shops with sequence-dependent setup times, job deterioration and learning effects. Neural Computing and Applications, 6, 1–15. https://doi.org/10.1007/s00521-018-3368-6
Behnamian, J., Fatemi Ghomi, S. M. T., & Zandieh, M. (2009). A multi-phase covering Pareto-optimal front method to multi-objective scheduling in a realistic hybrid flowshop using a hybrid metaheuristic. Expert Systems with Applica-tions, 36(8), 11057–11069. https://doi.org/10.1016/j.eswa.2009.02.080
Brunner, E., Dette, H., & Munk, A. (1997). Box-Type Approximations in Nonparametric Factorial Designs. Journal of the American Statistical Association, 92(440), 1494–1502. https://doi.org/10.1080/01621459.1997.10473671
Caldeira, R. H., & Gnanavelbabu, A. (2021). A simheuristic approach for the flexible job shop scheduling problem with stochastic processing times. SIMULATION, 97(3), 215–236. https://doi.org/10.1177/0037549720968891
Chen, C.-L., & Chen, C.-L. (2009). Bottleneck-based heuristics to minimize total tardiness for the flexible flow line with unrelated parallel machines. Computers & Industrial Engineering, 56(4), 1393–1401. https://doi.org/10.1016/j.cie.2008.08.016
Choi, S. H., & Wang, K. (2012). Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach. Computers & Industrial Engineering, 63(2), 362–373. https://doi.org/10.1016/j.cie.2012.04.001
Das, K. (2008). A comparative study of exponential distribution vs Weibull distribution in machine reliability analysis in a CMS design. Computers & Industrial Engineering, 54(1), 12–33. https://doi.org/10.1016/j.cie.2007.06.030
de León, A. D., Lalla-Ruiz, E., Melián-Batista, B., & Moreno-Vega, J. M. (2021). A simulation–optimization framework for enhancing robustness in bulk berth scheduling. Engineering Applications of Artificial Intelligence, 103, 104276. https://doi.org/10.1016/j.engappai.2021.104276
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II. In CEUR Workshop Proceedings (Vol. 1133, pp. 849–858). https://doi.org/10.1007/3-540-45356-3_83
Ebrahimi, M., Fatemi Ghomi, S. M. T., & Karimi, B. (2014). Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates. Applied Mathematical Modelling, 38(9–10), 2490–2504. https://doi.org/10.1016/j.apm.2013.10.061
Fu, Y., Zhou, M., Guo, X., & Qi, L. (2020). Scheduling Dual-Objective Stochastic Hybrid Flow Shop With Deteriorating Jobs via Bi-Population Evolutionary Algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(12), 5037–5048. https://doi.org/10.1109/TSMC.2019.2907575
Gonzalez-Neira, E. M., Ferone, D., Hatami, S., & Juan, A. A. (2017). A biased-randomized simheuristic for the distrib-uted assembly permutation flowshop problem with stochastic processing times. Simulation Modelling Practice and Theory, 79, 23–36. https://doi.org/10.1016/j.simpat.2017.09.001
González-Neira, E. M., García-Cáceres, R. G., Caballero-Villalobos, J. P., Molina-Sánchez, L. P., & Montoya-Torres, J. R. (2016). Stochastic flexible flow shop scheduling problem under quantitative and qualitative decision criteria. Computers & Industrial Engineering, 101, 128–144. https://doi.org/10.1016/j.cie.2016.08.026
González-Neira, E. M., Montoya-Torres, J. R., & Barrera, D. (2017). Flow-shop scheduling problem under uncertainties: Review and trends. International Journal of Industrial Engineering Computations, 8(4), 399–426. https://doi.org/10.5267/j.ijiec.2017.2.001
Han, W., Deng, Q., Gong, G., Zhang, L., & Luo, Q. (2021). Multi-objective evolutionary algorithms with heuristic de-coding for hybrid flow shop scheduling problem with worker constraint. Expert Systems with Applications, 168, 114282. https://doi.org/10.1016/j.eswa.2020.114282
Holthaus, O. (1999). Scheduling in job shops with machine breakdowns: An experimental study. Computers and Indus-trial Engineering, 36(1), 137–162. https://doi.org/10.1016/S0360-8352(99)00006-6
Hsieh, J.-C., Chang, P.-C., & Hsu, L.-C. (2003). Scheduling of drilling operations in printed circuit board factory☆. Computers & Industrial Engineering, 44(3), 461–473. https://doi.org/10.1016/S0360-8352(02)00231-0
Huang, Y., Deng, L., Wang, J., Qiu, W., & Liu, J. (2023). Modeling and solution for hybrid flow-shop scheduling prob-lem by two-stage stochastic programming. Expert Systems with Applications, 233. https://doi.org/10.1016/j.eswa.2023.120846
Ji, M., Yang, Y., Duan, W., Wang, S., & Liu, B. (2016). Scheduling of no-wait stochastic distributed assembly flowshop by hybrid PSO. 2016 IEEE Congress on Evolutionary Computation (CEC), 2649–2654. https://doi.org/10.1109/CEC.2016.7744120
Jiang, S., Liu, M., Hao, J., & Qian, W. (2015). A bi-layer optimization approach for a hybrid flow shop scheduling prob-lem involving controllable processing times in the steelmaking industry. Computers & Industrial Engineering, 87, 518–531. https://doi.org/10.1016/j.cie.2015.06.002
Juan, A. A., Barrios, B. B., Vallada, E., Riera, D., & Jorba, J. (2014). A simheuristic algorithm for solving the permuta-tion flow shop problem with stochastic processing times. Simulation Modelling Practice and Theory, 46, 101–117. https://doi.org/10.1016/j.simpat.2014.02.005
Juan, A. A., Faulin, J., Grasman, S. E., Rabe, M., & Figueira, G. (2015). A review of simheuristics: Extending metaheu-ristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2, 62–72. https://doi.org/10.1016/j.orp.2015.03.001
Kianfar, K., Fatemi Ghomi, S. M. T., & Oroojlooy Jadid, A. (2012). Study of stochastic sequence-dependent flexible flow shop via developing a dispatching rule and a hybrid GA. Engineering Applications of Artificial Intelligence, 25(3), 494–506. https://doi.org/10.1016/j.engappai.2011.12.004
Kim, D.-W., Na, D.-G., & Frank Chen, F. (2003). Unrelated parallel machine scheduling with setup times and a total weighted tardiness objective. Robotics and Computer-Integrated Manufacturing, 19(1–2), 173–181. https://doi.org/10.1016/S0736-5845(02)00077-7
Lin, J. T., & Chen, C.-M. (2015). Simulation optimization approach for hybrid flow shop scheduling problem in semi-conductor back-end manufacturing. Simulation Modelling Practice and Theory, 51, 100–114. https://doi.org/10.1016/j.simpat.2014.10.008
Lin, J. T., Chen, C.-M., Chiu, C.-C., & Fang, H.-Y. (2013). Simulation optimization with PSO and OCBA for semicon-ductor back-end assembly. Journal of Industrial and Production Engineering, 30(7), 452–460. https://doi.org/10.1080/21681015.2013.860926
Lin, Y.-K., & Huang, D.-H. (2020). Reliability analysis for a hybrid flow shop with due date consideration. Reliability Engineering & System Safety, 199, 105905. https://doi.org/10.1016/j.ress.2017.07.008
Liu, Q., Ullah, S., & Zhang, C. (2011). An improved genetic algorithm for robust permutation flowshop scheduling. The International Journal of Advanced Manufacturing Technology, 56(1–4), 345–354. https://doi.org/10.1007/s00170-010-3149-6
Liu, Y., Shen, W., Zhang, C., & Sun, X. (2023). Agent-based simulation and optimization of hybrid flow shop consider-ing multi-skilled workers and fatigue factors. Robotics and Computer-Integrated Manufacturing, 80. https://doi.org/10.1016/j.rcim.2022.102478
Minella, G., Ruiz, R., & Ciavotta, M. (2011). Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems. Computers & Operations Research, 38(11), 1521–1533. https://doi.org/10.1016/j.cor.2011.01.010
Mirabi, M., Ghomi, S. M. T. F., & Jolai, F. (2013). A two-stage hybrid flowshop scheduling problem in machine break-down condition. Journal of Intelligent Manufacturing, 24(1), 193–199. https://doi.org/10.1007/s10845-011-0553-1
Pinedo, M. L. (2012). Scheduling: Theory, algorithms and systems. In Springer (4th ed., Vol. 4). Springer Science & Business Media. https://doi.org/10.1007/978-1-4614-2361-4
Qin, W., Zhang, J., & Song, D. (2018). An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time. Journal of Intelligent Manufacturing, 29(4), 891–904. https://doi.org/10.1007/s10845-015-1144-3
Rahmani, D., Heydari, M., Makui, A., & Zandieh, M. (2013). A new approach to reducing the effects of stochastic dis-ruptions in flexible flow shop problems with stability and nervousness. International Journal of Management Sci-ence and Engineering Management, 8(3), 173–178. https://doi.org/10.1080/17509653.2013.812332
Rajendran, C., & Chaudhuri, D. (1992). Scheduling in n-job, m-stage flowshop with parallel processors to minimize makespan. International Journal of Production Economics, 27(2), 137–143. https://doi.org/10.1016/0925-5273(92)90006-S
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