Abedi, M., Chiong, R., Noman, N., & Zhang, R. (2020). A multi-population, multi-objective memetic algorithm for energy-efficient job-shop scheduling with deteriorating machines. Expert Systems with Applications, 157, 113348. https://doi.org/10.1016/j.eswa.2020.113348
Al-Khamis, T., & M’Hallah, R. (2011). A two-stage stochastic programming model for the parallel machine scheduling problem with machine capacity. Computers and Operations Research, 38(12), 1747–1759. https://doi.org/10.1016/j.cor.2011.01.017
Alchieri, E., Dotti, F., & Pedone, F. (2018). Early scheduling in parallel state machine replication. SoCC 2018 - Proceedings of the 2018 ACM Symposium on Cloud Computing, 82–94. https://doi.org/10.1145/3267809.3267825
Arık, O. A., & Toksarı, M. D. (2018). Multi-objective fuzzy parallel machine scheduling problems under fuzzy job deterioration and learning effects. International Journal of Production Research, 56(7), 2488–2505. https://doi.org/10.1080/00207543.2017.1388932
Brier, J., & lia dwi jayanti. (2020). Sequence-dependent group scheduling problem on unrelated-parallel machines. 21(1), 1–9. http://journal.um-surabaya.ac.id/index.php/JKM/article/view/2203
Chanas, S., & Kasperski, A. (2001). Minimizing maximum lateness in a single machine scheduling problem with fuzzy processing times and fuzzy due dates. Engineering Applications of Artificial Intelligence, 14(3), 377–386. https://doi.org/10.1016/S0952-1976(01)00011-2
Chen, S., & Wang, C. (2008). Fuzzy distance of trapezoidal fuzzy numbers and application. International Journal of Innovative Computing, Information and Control, 4(6), 1445–1454.
Ding, J., Shen, L., Lü, Z., & Peng, B. (2019). Parallel machine scheduling with completion-time-based criteria and sequence-dependent deterioration. Computers and Operations Research, 103, 35–45. https://doi.org/10.1016/j.cor.2018.10.016
Esmaili, M., Amjady, N., & Shayanfar, H. A. (2011). Multi-objective congestion management by modified augmented ε-constraint method. Applied Energy, 88(3), 755–766. https://doi.org/10.1016/j.apenergy.2010.09.014
Expósito-Izquierdo, C., Angel-Bello, F., Melián-Batista, B., Alvarez, A., & Báez, S. (2019). A metaheuristic algorithm and simulation to study the effect of learning or tiredness on sequence-dependent setup times in a parallel machine scheduling problem. Expert Systems with Applications, 117, 62–74. https://doi.org/10.1016/j.eswa.2018.09.041
G, M., & K, F. (2020). An improved version of the augmented epsilon-constraint method (AUGMECON2) for finding the exact Pareto set in Multi-Objective Integer Programming problems. 105034.
Ghanbari, H., Fooeik, A. M. L., Eskorouchi, A., & Mohammadi, E. (2022). Investigating the effect of US dollar, gold and oil prices on the stock market. Journal of Future Sustainability, 2(3), 97–104. https://doi.org/10.5267/j.jfs.2022.9.009
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. In Annals of Discrete Mathematics (Vol. 5, Issue C, pp. 287–326). https://doi.org/10.1016/S0167-5060(08)70356-X
Hemmat Esfe, M., Hajmohammad, H., Toghraie, D., Rostamian, H., Mahian, O., & Wongwises, S. (2017). Multi-objective optimization of nanofluid flow in double tube heat exchangers for applications in energy systems. Energy, 137, 160–171. https://doi.org/10.1016/j.energy.2017.06.104
Hosseini, S. A., Amjady, N., Shafie-khah, M., & Catalão, J. P. S. (2016). A new multi-objective solution approach to solve transmission congestion management problem of energy markets. Applied Energy, 165, 462–471. https://doi.org/10.1016/j.apenergy.2015.12.101
Hsu, C. J., Kuo, W. H., & Yang, D. L. (2011). Unrelated parallel machine scheduling with past-sequence-dependent setup time and learning effects. Applied Mathematical Modelling, 35(3), 1492–1496. https://doi.org/10.1016/j.apm.2010.09.026
Huy, T. H. B., Dinh, H. T., & Kim, D. (2023). Multi-objective framework for a home energy management system with the integration of solar energy and an electric vehicle using an augmented ε-constraint method and lexicographic optimization. Sustainable Cities and Society, 88(July 2022), 104289. https://doi.org/10.1016/j.scs.2022.104289
Jabir, E., Panicker, V. V., & Sridharan, R. (2015). Multi-objective Optimization Model for a Green Vehicle Routing Problem. Procedia - Social and Behavioral Sciences, 189, 33–39. https://doi.org/10.1016/j.sbspro.2015.03.189
Javadi, M., Lotfi, M., Osorio, G. J., Ashraf, A., Nezhad, A. E., Gough, M., & Catalao, J. P. S. (2020). A Multi-Objective Model for Home Energy Management System Self-Scheduling using the Epsilon-Constraint Method. Proceedings - 2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020, 175–180. https://doi.org/10.1109/CPE-POWERENG48600.2020.9161526
Ji, M., & Cheng, T. C. E. (2008). Parallel-machine scheduling with simple linear deterioration to minimize total completion time. European Journal of Operational Research, 188(2), 342–347. https://doi.org/10.1016/j.ejor.2007.04.050
Khoshabi, P., Nejati, E., Ahmadi, S. F., Chegini, A., Makui, A., & Ghousi, R. (2020). Developing a multi-criteria decision making approach to compare types of classroom furniture considering mismatches for anthropometric measures of university students. PLoS ONE, 15(9 September), 1–25. https://doi.org/10.1371/journal.pone.0239297
Kim, H. J., & Lee, J. H. (2021). Scheduling uniform parallel dedicated machines with job splitting, sequence-dependent setup times, and multiple servers. Computers and Operations Research, 126, 105115. https://doi.org/10.1016/j.cor.2020.105115
L.A.Zadeh. (1965). Fuzzy Sets. Department of Electrical Engineering and Electronics L~esearch Laboratory, University of California, Berkeley, California A, 353, 1. https://doi.org/10.1061/9780784413616.194
Laumanns, M., Thiele, L., & Zitzler, E. (2006). An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method. European Journal of Operational Research, 169(3), 932–942. https://doi.org/10.1016/j.ejor.2004.08.029
Lee, W. C., Chuang, M. C., & Yeh, W. C. (2012). Uniform parallel-machine scheduling to minimize makespan with position-based learning curves. Computers and Industrial Engineering, 63(4), 813–818. https://doi.org/10.1016/j.cie.2012.05.003
Liao, L. W., & Sheen, G. J. (2008). Parallel machine scheduling with machine availability and eligibility constraints. European Journal of Operational Research, 184(2), 458–467. https://doi.org/10.1016/j.ejor.2006.11.027
Liu, B., & Liu, Y. K. (2002). Expected value of fuzzy variable and fuzzy expected value models. IEEE Transactions on Fuzzy Systems, 10(4), 445–450. https://doi.org/10.1109/TFUZZ.2002.800692
Liu, G., Shah, R., & Schroeder, R. G. (2006). Linking work design to mass customization: A sociotechnical systems perspective. Decision Sciences, 37(4), 519–545. https://doi.org/10.1111/j.1540-5414.2006.00137.x
Liu, M. (2013). Parallel-machine scheduling with past-sequence-dependent delivery times and learning effect. Applied Mathematical Modelling, 37(23), 9630–9633. https://doi.org/10.1016/j.apm.2013.05.025
Liu, M., Zheng, F., Wang, S., & Xu, Y. (2013). Approximation algorithms for parallel machine scheduling with linear deterioration. Theoretical Computer Science, 497, 108–111. https://doi.org/10.1016/j.tcs.2012.01.020
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems. Applied Mathematics and Computation, 213(2), 455–465. https://doi.org/10.1016/j.amc.2009.03.037
Mazdeh, M. M., Zaerpour, F., Zareei, A., & Hajinezhad, A. (2010). Parallel machines scheduling to minimize job tardiness and machine deteriorating cost with deteriorating jobs. Applied Mathematical Modelling, 34(6), 1498–1510. https://doi.org/10.1016/j.apm.2009.08.023
Meyr, H. (2002). Simultaneous lotsizing and scheduling on parallel machines. European Journal of Operational Research, 139(2), 277–292. https://doi.org/10.1016/S0377-2217(01)00373-3
Mor, B., Mosheiov, G., & Shapira, D. (2020). Flowshop scheduling with learning effect and job rejection. Journal of Scheduling, 23(6), 631–641. https://doi.org/10.1007/s10951-019-00612-y
Peng, J., & Liu, B. (2004). Parallel machine scheduling models with fuzzy processing times. Information Sciences, 166(1–4), 49–66. https://doi.org/10.1016/j.ins.2003.05.012
Petchrompo, S., Wannakrairot, A., & Parlikad, A. K. (2022). Pruning Pareto optimal solutions for multi-objective portfolio asset management. European Journal of Operational Research, 297(1), 203–220. https://doi.org/10.1016/j.ejor.2021.04.053
Pinto, J. L. Q., Matias, J. C. O., Pimentel, C., Azevedo, S. G., & Govindan, K. (2018). Lean Manufacturing and Kaizen. https://doi.org/10.1007/978-3-319-77016-1_2
Prot, D., Bellenguez-Morineau, O., & Lahlou, C. (2013). New complexity results for parallel identical machine scheduling problems with preemption, release dates and regular criteria. European Journal of Operational Research, 231(2), 282–287. https://doi.org/10.1016/j.ejor.2013.05.041
Przybylski, B. (2018). A new model of parallel-machine scheduling with integral-based learning effect. Computers and Industrial Engineering, 121(May), 189–194. https://doi.org/10.1016/j.cie.2018.05.035
Rostami, M., Pilerood, A. E., & Mazdeh, M. M. (2015). Multi-objective parallel machine scheduling problem with job deterioration and learning effect under fuzzy environment. Computers and Industrial Engineering, 85, 206–215. https://doi.org/10.1016/j.cie.2015.03.022
Rostami, M., & Sabripoor, A. (2022). Optimization of green hybrid flow shop scheduling problem with considering batch delivery system. Modern Research in Decision Making, 7(4), 126–155. http://journal.saim.ir/article_701950.html
Ruiz-Torres, A. J., Paletta, G., & Pérez, E. (2013). Parallel machine scheduling to minimize the makespan with sequence dependent deteriorating effects. Computers and Operations Research, 40(8), 2051–2061. https://doi.org/10.1016/j.cor.2013.02.018
Sariçiçek, I., & Çelik, Ç. (2011). Two meta-heuristics for parallel machine scheduling with job splitting to minimize total tardiness. Applied Mathematical Modelling, 35(8), 4117–4126. https://doi.org/10.1016/j.apm.2011.02.035
Sekkal, N., & Belkaid, F. (2020). A multi-objective simulated annealing to solve an identical parallel machine scheduling problem with deterioration effect and resources consumption constraints. In Journal of Combinatorial Optimization (Vol. 40, Issue 3). Springer US. https://doi.org/10.1007/s10878-020-00607-y
Siemieniuch, C. E., Sinclair, M. A., & Henshaw, M. J. C. (2015). Global drivers, sustainable manufacturing and systems ergonomics. Applied Ergonomics, 51(November), 104–119. https://doi.org/10.1016/j.apergo.2015.04.018
Spielhofer, R., Schwaab, J., & Grêt-Regamey, A. (2022). How Spatial Policies Can Leverage Energy Transitions - Finding Pareto-Optimal Solutions for Wind Turbine Locations with Evolutionary Multi-Objective Optimization. SSRN Electronic Journal, 142(August 2022), 220–232. https://doi.org/10.2139/ssrn.4220641
Stanovov, V., Akhmedova, S., & Semenkin, E. (2020). Combined fitness–violation epsilon constraint handling for differential evolution. Soft Computing, 24(10), 7063–7079. https://doi.org/10.1007/s00500-020-04835-6
Tanaka, S. (1992). 3 Fuzzy Mathematical Programming. I.
Toksari, M. D., & Güner, E. (2009). Parallel machine earliness/tardiness scheduling problem under the effects of position based learning and linear/nonlinear deterioration. Computers and Operations Research, 36(8), 2394–2417. https://doi.org/10.1016/j.cor.2008.09.012
Torabzadeh, S. A., Nejati, E., Aghsami, A., & Rabbani, M. (2022). A dynamic multi-objective green supply chain network design for perishable products in uncertain environments, the coffee industry case study. International Journal of Management Science and Engineering Management, 17(3), 220–237. https://doi.org/10.1080/17509653.2022.2055672
Unlu, Y., & Mason, S. J. (2010). Evaluation of mixed integer programming formulations for non-preemptive parallel machine scheduling problems. Computers and Industrial Engineering, 58(4), 785–800. https://doi.org/10.1016/j.cie.2010.02.012
Vallada, E., & Ruiz, R. (2011). A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times. European Journal of Operational Research, 211(3), 612–622. https://doi.org/10.1016/j.ejor.2011.01.011
Wang, X., & Edwin Cheng, T. C. (2007). Single-machine scheduling with deteriorating jobs and learning effects to minimize the makespan. European Journal of Operational Research, 178(1), 57–70. https://doi.org/10.1016/j.ejor.2006.01.017
Wang, X., Li, Z., Chen, Q., & Mao, N. (2020). Meta-heuristics for unrelated parallel machines scheduling with random rework to minimize expected total weighted tardiness. Computers and Industrial Engineering, 145(100), 106505. https://doi.org/10.1016/j.cie.2020.106505
Wu, R., Li, Y., Guo, S., & Xu, W. (2018). Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm. Advances in Mechanical Engineering, 10(10), 1–14. https://doi.org/10.1177/1687814018804096
Wu, X., Shen, X., & Li, C. (2019). The flexible job-shop scheduling problem considering deterioration effect and energy consumption simultaneously. Computers and Industrial Engineering, 135(June), 1004–1024. https://doi.org/10.1016/j.cie.2019.06.048
Xing, X., Yan, Y., Zhang, H., Long, Y., Wang, Y., & Liang, Y. (2019). Optimal design of distributed energy systems for industrial parks under gas shortage based on augmented ε-constraint method. Journal of Cleaner Production, 218, 782–795. https://doi.org/10.1016/j.jclepro.2019.02.052
Yang, S. J. (2013). Unrelated parallel-machine scheduling with deterioration effects and deteriorating multi-maintenance activities for minimizing the total completion time. Applied Mathematical Modelling, 37(5), 2995–3005. https://doi.org/10.1016/j.apm.2012.07.029
Yang, Z., Cai, X., & Fan, Z. (2014). Epsilon constrained method for constrained multiobjective optimization problems: Some preliminary results. GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference, 1181–1185. https://doi.org/10.1145/2598394.2610012
Zhang, M., & Luo, C. (2013). Parallel-machine scheduling with deteriorating jobs, rejection and a fixed non-availability interval. Applied Mathematics and Computation, 224, 405–411. https://doi.org/10.1016/j.amc.2013.07.003
Zhu, H., & Zhang, J. (2009). A credibility-based fuzzy programming model for APP problem. 2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009, 1, 455–459. https://doi.org/10.1109/AICI.2009.204