8
How to cite this paper
APA: Azevedo, B., Montanño-Vega, R., Varela, M & Pereira, A. (2023). Bio-inspired multi-objective algorithms applied on production scheduling problems. International Journal of Industrial Engineering Computations, 14(2), 415-436.
Chicago/Turabian: Azevedo, B., Montanño-Vega, R., Varela, M & Pereira, A. 2023. "Bio-inspired multi-objective algorithms applied on production scheduling problems." International Journal of Industrial Engineering Computations 14, no. 2 (2023): 415-436.
AMA: Azevedo, B., Montanño-Vega, R., Varela, M & Pereira, A. Bio-inspired multi-objective algorithms applied on production scheduling problems. International Journal of Industrial Engineering Computations. 2023;14(2):415-436.
References
Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Perez, J. M., & Perona I. (2013). An extensive comparative study of cluster validity indices. Pattern Recognition, 46(1), 243–256, (DOI: 10.1016/j.patcog.2012.07.021).
Azevedo, B. F. (2020). Study of genetic algorithms for optimization problems. Master’s thesis, Instituto Politécnico de Bragança Escola Superior de Tecnologia e Gestão, Portugal, Bragança, Portugal.
Azevedo, B. F., Varela, M. L. R., & Pereira, A. I. (2022). Production scheduling using multi-objective optimization and cluster approaches. In: Abraham A et al (eds) Innovations in Bio-Inspired Computing and Applications IBICA 2021 - Lecture Notes in Networks and Systems 419, (DOI: 10.1007/ 978-3-030-96299-9 12).
Bansal, J. C., Singh, P. K., & Nikhil, R. P. (2019). Evolutionary and swarm intelligence algorithms. Studies in Computational Intelligence, (DOI: 10.1007/ 978-3-319-91341-4).
Barenji, R.V., Barenji, A.V., & Hashemipour, M. (2014). A multi-agent RFID-enabled distributed control system for a flexible manufacturing shop. The International Journal of Advanced Manufacturing Technology, 71(9-12), 1773–1791, (DOI: 10.1007/s00170-013-5597-2).
Borangiu, T., Morariu, O., Raileanu, S., Trentesaux, D., Leitão, P., & Barata, J. (2020) Digital transformation of manufacturing. industry of the future with cyber-physical production systems. Romanian Journal of Information Science and Technology 23, 3–37.
Chaouch, I., Driss, O. B., & Ghedira, K. (2017) A modified Ant Colony Optimization algorithm for the Distributed Job shop Scheduling Problem. Procedia Computer Science, 112, 296–305, (DOI: 10.1016/j.procs.2017.08.267).
Chen, J., Qi, X., Chen, L., Chen, F., & Cheng, G. (2020) Quantum-inspired ant lion optimized hybrid k-means for cluster analysis and intrusion detection. Knowledge-Based Systems, 203, 106167, (DOI: 10.1016/j.knosys.2020.106167).
Chen, Y., Guan, Z., Wang, C., Chou, F., & Yue, L. (2022). Bi-objective optimization of identical parallel machine scheduling with flexible maintenance and job release times. International Journal of Industrial Engineering Computations, 13(4), 457–472, (DOI: 10.5267/j.ijiec.2022.8.003).
Coello-Coello, C. A., & Lechuga, M. S. (2002). MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No.02TH8600), 2, pp 1051–1056 vol.2, (DOI: 10.1109/CEC.2002.1004388).
Coello-Coello, C. A., Pulido, G. T., & Lechuga, M. S. (2004) Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256–279, (DOI: 10.1109/TEVC.2004.826067).
Dai, M., Tang, D., Giret, A., & Salido, M. A. (2019). Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robotics and Computer-Integrated Manufacturing, 59, 143–157, (DOI: 10.1016/j.rcim.2019.04.006).
Deb, K. (2011). Multi-objective optimization using evolutionary algorithms: An introduction. In: Multi-objective Evolutionary Optimisation for Product Design and Manufacturing, Wang, L. and Amos, H. C. Ng and Deb, K. (eds.), 1st edn, Springer-Verlag London.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197, (DOI: 10.1109/4235.996017).
Ezugwu, A. E., Shukla, A. K., Agbaje, M. B., Oyelade, O. N., José-García, A., & Agushaka, J. O. (2021). Automatic clustering algorithms: a systematic review and bibliometric analysis of relevant literature. Neural Computing and Applications, 33(11), 6247-6306.
Fu, Y., Ding, J., Wang, H., & Wang, J. (2018). Two-objective stochastic flow-shop scheduling with deteriorating and learning effect in Industry 4.0-based manufacturing system. Applied Soft Computing, 68, 847-855.
Goel, L., Raman, S., Dora, S. S., Bhutani, A., Aditya, A. S., & Mehta, A. (2020) Hybrid computational intelligence algorithms and their applications to detect food quality. Artificial Intelligence Review, 53(2), 1415–1440, (DOI: 10.1007/s10462-019-09705-8).
Gong, D., Han, Y., & Sun, J. (2018). A novel hybrid multi-objective artificial bee colony algorithm for blocking lot-streaming flow shop scheduling problems. Knowledge-Based Systems, 148, 115-130. (DOI: 10.1016/j.knosys.2018.02.029).
Jia, S., Yi, J., Yang, G., Du, B., & Zhu, J. (2013). A multi-objective optimisation algorithm for the hot rolling batch scheduling problem. International Journal of Production Research, 51(3), 667–681, (DOI: 10.1080/00207543.2011.654138).
Kagermann, H., Lukas, W., & Wahlster, W. (2011). Industrie 4.0: Mitdem Internet der Dinge auf dem Weg zur 4. industriellen Revolution. VDI nachrichten 13.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks, 4, 1942–1948 vol.4, (DOI: 10.1109/ICNN.1995.488968).
Kok, J., Gonzalez, F., Kelson, N., & Periaux, J. (2011). An FPGA-based approach to multi-objective evolutionary algorithm for multi-disciplinary design optimisation. In Poloni, C, Gauger, N, Periaux, J, Quagliarella, D, & Giannakoglou, K (Eds.) Proceedings of the International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems (Eurogen 2011). CIRA - Italian Aerospace Research Centre, Italy, pp. 1-10.
Lin, D. Y., & Huang, T. Y. (2021) A hybrid metaheuristic for the unrelated parallel machine scheduling problem. Mathematics, 9(7), 768, (DOI:10.3390/math9070768).
Lu, C., Xiao, S., Li, X., & Gao, L. (2016) An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production. Advances in Engineering Software, 99, 161–176, (DOI: 10.1016/j.advengsoft. 2016.06.004).
Luo, S., Zhang, L., & Fan, Y. (2019) Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization. Journal of Cleaner Production, 234, 1365–1384, (DOI: 10.1016/j.jclepro.2019. 06.151).
MATLAB (2019). The mathworks inc 2019a. https://www.mathworks.com
Miettinen, K. (1998) Nonlinear multiobjective optimization, 1st ed. International Series in Operations Research & Management Science, Springer.
Mirjalili, S. (2022) Multi-objective grey wolf optimizer (mogwo). https://www.mathworks.com/matlabcentral/fileexchange/55979-multi-objective-grey-wolf-optimizer-mogwo, retrieved February 2, 2022.
Mirjalili, S., Mirjalili, S. M., & Lewis A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61, (DOI: 10.1016/j.advengsoft.2013.12.007).
Mirjalili, S., Saremi, S., Mirjalili, S. M., & dos S Coelho, L. (2016). Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Systems with Applications, 47, 106–119, (DOI: 10.1016/j.eswa.2015.10.039).
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.
Pinedo, M. L. (2012). Scheduling: theory, algorithms, and systems. Springer (DOI:10.1007/978-1-4614-2361-4).
Piroozfard, H., Wong, K.Y., & Wong, W. P. (2018). Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm. Resources, Conservation and Recycling 128, 267–283, (DOI: 10.1016/j.resconrec.2016.12.001).
Qaddoura, R., Faris, H., & Aljarah, I. (2021). An efficient evolutionary algorithm with a nearest neighbour search technique for clustering analysis. Journal of Ambient Intelligence and Humanized Computing, 12, 8387–8412, (DOI: 10.1007/s12652-020-02570-2).
Qin, H., Fan, P., Tang, H., Huang, P., Fang, B., & Pan, S. (2019). An effective hybrid discrete grey wolf optimizer for the casting production scheduling problem with multi-objective and multi-constraint. Computers & Industrial Engineering, 128, 458-476. (DOI: 10.1016/j.cie.2018.12.061).
Reis, P. C. S. O. (2020). Ferramenta de apoio ao escalonamento da produção. Master’s thesis, Instituto Superior de Engenharia do Porto - Departamento de Engenharia Mecânica.
Safarzadeha, H., & Niakia, S. T. A. (2023). Unrelated parallel machine scheduling with machine processing cost. International Journal of Industrial Engineering Computations, 14(1), 33–48, (DOI: 10.5267/j.ijiec.2022.10.004).
Santos, A. S., Madureira A. M., & Varela M. L. R. (2015). An ordered heuristic for the allocation of resources in unrelated parallel machines. International Journal of Industrial Engineering Computations, 6(2)
Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: from theory to algorithms. Cambridge University Press.
Sheikhalishahi, M., Eskandari, N., Mashayekhi, A., & Azadeh A. (2019). Multi-objective open shop scheduling by considering human error and preventive maintenance. Applied Mathematical Modelling, 67, 573–587, (DOI: 10.1016/j. apm.2018.11.015).
Singh, T. (2021). A novel data clustering approach based on whale optimization algorithm. Expert Systems, 38, 8387–8412, (DOI: doi.org/10.1111/exsy.12657).
Sivanandam, S. N., & Deepa, S. N. (2008). Introduction to Genetic Algorithms, 1st ed. Springer, (DOI: 10.1007/978-3-540-73190-0).
Varela, M. L. R., & Ribeiro R. A. (2014). Distributed manufacturing scheduling based on a dynamic multi-criteria decision model. Springer, (DOI:10.1007/978-3-319-06323-2_6).
Varela, M. L., Putnik, G. D., Manupati, V. K., Rajyalakshmi, G., Trojanowska, J., & Machado, J. (2021). Integrated process planning and scheduling in networked manufacturing systems for I4. 0: a review and framework proposal. Wireless Networks, 27(3), 1587-1599. Springer. (DOI: 10.1007/s11276-019-02082-8).
Varela, M. L. R., Putnik, G. D., Alves, C. F., Lopes, N., & Cruz-Cunha, M. M. (2022). A Systematic Review of Manufacturing Scheduling for the Industry 4.0. 1st International Symposium on Industrial Engineering and Automation (ISIEA 2022), Managing and Implementing the Digital Transformation, 21st-22nd June 2022, Bozen-Bolzano, Italy. Lecture Notes in Networks and Systems (pp. 237-249), Springer.
Wang, Q., Wang, X., Luo, H., & Xiong, J. (2020) An improved multi-objective evolutionary approach for aerospace shell production scheduling problem. Symmetry, 12(4), (DOI: 10.3390/sym12040509).
Wang, Z., Zhang, J., & Yang, S. (2019). An improved particle swarm optimization algorithm for dynamic job shop scheduling problems with random job arrivals. Swarm and Evolutionary Computation, 51, 100594, (DOI: 10.1016/j. swevo.2019.100594).
Wolpert, D. H., & Macready, W. G., (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82, (DOI: 10.1109/ 4235.585893).
Yapiz (2022a) Evolutionary clustering and automatic clustering. (URL: https://www.mathworks.com/matlabcentral/fileexchange/52865-evolutionary-clustering-and-automatic-clustering), retrieved February 2, 2022.
Yapiz (2022b) Multi-objective particle swarm optimization (MOPSO). (URL: https://www.mathworks.com/matlabcentral/fileexchange/52870-multi-objective-particle-swarm-optimization-mopso), retrieved February 2, 2022.
Zhang, J., Ding, G., Zou, Y., Qin, S., & Fu, J. (2019) Review of job shop scheduling research and its new perspectives under Industry 4.0. Journal of Intelligent Manufacturing, 30(4), 1809–1830, (DOI: 10.1007/s10845-017-1350-2).
Zhang, Q., & Li, H. (2007). MOEA/D: A multi-objective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712-731, (DOI: 10.1109/TEVC.2007.892759).
Zhang, S., Tang, F., Li, X., Liu, J., & Zhang, B. (2021). A hybrid multi-objective approach for real-time flexible production scheduling and rescheduling under dynamic environment in Industry 4.0 context. Computers & Operations Research, 132, 105267, (DOI: 10.1016/j.cor.2021.105267).
Zheng, F., Jin, K., Xu, Y., & Liu, M. (2022). Unrelated parallel machine scheduling with processing cost, machine eligibility and order splitting. Computers & Industrial Engineering, 171. (DOI: 10.1016/j.cie.2022.108483).
Azevedo, B. F. (2020). Study of genetic algorithms for optimization problems. Master’s thesis, Instituto Politécnico de Bragança Escola Superior de Tecnologia e Gestão, Portugal, Bragança, Portugal.
Azevedo, B. F., Varela, M. L. R., & Pereira, A. I. (2022). Production scheduling using multi-objective optimization and cluster approaches. In: Abraham A et al (eds) Innovations in Bio-Inspired Computing and Applications IBICA 2021 - Lecture Notes in Networks and Systems 419, (DOI: 10.1007/ 978-3-030-96299-9 12).
Bansal, J. C., Singh, P. K., & Nikhil, R. P. (2019). Evolutionary and swarm intelligence algorithms. Studies in Computational Intelligence, (DOI: 10.1007/ 978-3-319-91341-4).
Barenji, R.V., Barenji, A.V., & Hashemipour, M. (2014). A multi-agent RFID-enabled distributed control system for a flexible manufacturing shop. The International Journal of Advanced Manufacturing Technology, 71(9-12), 1773–1791, (DOI: 10.1007/s00170-013-5597-2).
Borangiu, T., Morariu, O., Raileanu, S., Trentesaux, D., Leitão, P., & Barata, J. (2020) Digital transformation of manufacturing. industry of the future with cyber-physical production systems. Romanian Journal of Information Science and Technology 23, 3–37.
Chaouch, I., Driss, O. B., & Ghedira, K. (2017) A modified Ant Colony Optimization algorithm for the Distributed Job shop Scheduling Problem. Procedia Computer Science, 112, 296–305, (DOI: 10.1016/j.procs.2017.08.267).
Chen, J., Qi, X., Chen, L., Chen, F., & Cheng, G. (2020) Quantum-inspired ant lion optimized hybrid k-means for cluster analysis and intrusion detection. Knowledge-Based Systems, 203, 106167, (DOI: 10.1016/j.knosys.2020.106167).
Chen, Y., Guan, Z., Wang, C., Chou, F., & Yue, L. (2022). Bi-objective optimization of identical parallel machine scheduling with flexible maintenance and job release times. International Journal of Industrial Engineering Computations, 13(4), 457–472, (DOI: 10.5267/j.ijiec.2022.8.003).
Coello-Coello, C. A., & Lechuga, M. S. (2002). MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No.02TH8600), 2, pp 1051–1056 vol.2, (DOI: 10.1109/CEC.2002.1004388).
Coello-Coello, C. A., Pulido, G. T., & Lechuga, M. S. (2004) Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256–279, (DOI: 10.1109/TEVC.2004.826067).
Dai, M., Tang, D., Giret, A., & Salido, M. A. (2019). Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robotics and Computer-Integrated Manufacturing, 59, 143–157, (DOI: 10.1016/j.rcim.2019.04.006).
Deb, K. (2011). Multi-objective optimization using evolutionary algorithms: An introduction. In: Multi-objective Evolutionary Optimisation for Product Design and Manufacturing, Wang, L. and Amos, H. C. Ng and Deb, K. (eds.), 1st edn, Springer-Verlag London.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197, (DOI: 10.1109/4235.996017).
Ezugwu, A. E., Shukla, A. K., Agbaje, M. B., Oyelade, O. N., José-García, A., & Agushaka, J. O. (2021). Automatic clustering algorithms: a systematic review and bibliometric analysis of relevant literature. Neural Computing and Applications, 33(11), 6247-6306.
Fu, Y., Ding, J., Wang, H., & Wang, J. (2018). Two-objective stochastic flow-shop scheduling with deteriorating and learning effect in Industry 4.0-based manufacturing system. Applied Soft Computing, 68, 847-855.
Goel, L., Raman, S., Dora, S. S., Bhutani, A., Aditya, A. S., & Mehta, A. (2020) Hybrid computational intelligence algorithms and their applications to detect food quality. Artificial Intelligence Review, 53(2), 1415–1440, (DOI: 10.1007/s10462-019-09705-8).
Gong, D., Han, Y., & Sun, J. (2018). A novel hybrid multi-objective artificial bee colony algorithm for blocking lot-streaming flow shop scheduling problems. Knowledge-Based Systems, 148, 115-130. (DOI: 10.1016/j.knosys.2018.02.029).
Jia, S., Yi, J., Yang, G., Du, B., & Zhu, J. (2013). A multi-objective optimisation algorithm for the hot rolling batch scheduling problem. International Journal of Production Research, 51(3), 667–681, (DOI: 10.1080/00207543.2011.654138).
Kagermann, H., Lukas, W., & Wahlster, W. (2011). Industrie 4.0: Mitdem Internet der Dinge auf dem Weg zur 4. industriellen Revolution. VDI nachrichten 13.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks, 4, 1942–1948 vol.4, (DOI: 10.1109/ICNN.1995.488968).
Kok, J., Gonzalez, F., Kelson, N., & Periaux, J. (2011). An FPGA-based approach to multi-objective evolutionary algorithm for multi-disciplinary design optimisation. In Poloni, C, Gauger, N, Periaux, J, Quagliarella, D, & Giannakoglou, K (Eds.) Proceedings of the International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems (Eurogen 2011). CIRA - Italian Aerospace Research Centre, Italy, pp. 1-10.
Lin, D. Y., & Huang, T. Y. (2021) A hybrid metaheuristic for the unrelated parallel machine scheduling problem. Mathematics, 9(7), 768, (DOI:10.3390/math9070768).
Lu, C., Xiao, S., Li, X., & Gao, L. (2016) An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production. Advances in Engineering Software, 99, 161–176, (DOI: 10.1016/j.advengsoft. 2016.06.004).
Luo, S., Zhang, L., & Fan, Y. (2019) Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization. Journal of Cleaner Production, 234, 1365–1384, (DOI: 10.1016/j.jclepro.2019. 06.151).
MATLAB (2019). The mathworks inc 2019a. https://www.mathworks.com
Miettinen, K. (1998) Nonlinear multiobjective optimization, 1st ed. International Series in Operations Research & Management Science, Springer.
Mirjalili, S. (2022) Multi-objective grey wolf optimizer (mogwo). https://www.mathworks.com/matlabcentral/fileexchange/55979-multi-objective-grey-wolf-optimizer-mogwo, retrieved February 2, 2022.
Mirjalili, S., Mirjalili, S. M., & Lewis A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61, (DOI: 10.1016/j.advengsoft.2013.12.007).
Mirjalili, S., Saremi, S., Mirjalili, S. M., & dos S Coelho, L. (2016). Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Systems with Applications, 47, 106–119, (DOI: 10.1016/j.eswa.2015.10.039).
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.
Pinedo, M. L. (2012). Scheduling: theory, algorithms, and systems. Springer (DOI:10.1007/978-1-4614-2361-4).
Piroozfard, H., Wong, K.Y., & Wong, W. P. (2018). Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm. Resources, Conservation and Recycling 128, 267–283, (DOI: 10.1016/j.resconrec.2016.12.001).
Qaddoura, R., Faris, H., & Aljarah, I. (2021). An efficient evolutionary algorithm with a nearest neighbour search technique for clustering analysis. Journal of Ambient Intelligence and Humanized Computing, 12, 8387–8412, (DOI: 10.1007/s12652-020-02570-2).
Qin, H., Fan, P., Tang, H., Huang, P., Fang, B., & Pan, S. (2019). An effective hybrid discrete grey wolf optimizer for the casting production scheduling problem with multi-objective and multi-constraint. Computers & Industrial Engineering, 128, 458-476. (DOI: 10.1016/j.cie.2018.12.061).
Reis, P. C. S. O. (2020). Ferramenta de apoio ao escalonamento da produção. Master’s thesis, Instituto Superior de Engenharia do Porto - Departamento de Engenharia Mecânica.
Safarzadeha, H., & Niakia, S. T. A. (2023). Unrelated parallel machine scheduling with machine processing cost. International Journal of Industrial Engineering Computations, 14(1), 33–48, (DOI: 10.5267/j.ijiec.2022.10.004).
Santos, A. S., Madureira A. M., & Varela M. L. R. (2015). An ordered heuristic for the allocation of resources in unrelated parallel machines. International Journal of Industrial Engineering Computations, 6(2)
Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: from theory to algorithms. Cambridge University Press.
Sheikhalishahi, M., Eskandari, N., Mashayekhi, A., & Azadeh A. (2019). Multi-objective open shop scheduling by considering human error and preventive maintenance. Applied Mathematical Modelling, 67, 573–587, (DOI: 10.1016/j. apm.2018.11.015).
Singh, T. (2021). A novel data clustering approach based on whale optimization algorithm. Expert Systems, 38, 8387–8412, (DOI: doi.org/10.1111/exsy.12657).
Sivanandam, S. N., & Deepa, S. N. (2008). Introduction to Genetic Algorithms, 1st ed. Springer, (DOI: 10.1007/978-3-540-73190-0).
Varela, M. L. R., & Ribeiro R. A. (2014). Distributed manufacturing scheduling based on a dynamic multi-criteria decision model. Springer, (DOI:10.1007/978-3-319-06323-2_6).
Varela, M. L., Putnik, G. D., Manupati, V. K., Rajyalakshmi, G., Trojanowska, J., & Machado, J. (2021). Integrated process planning and scheduling in networked manufacturing systems for I4. 0: a review and framework proposal. Wireless Networks, 27(3), 1587-1599. Springer. (DOI: 10.1007/s11276-019-02082-8).
Varela, M. L. R., Putnik, G. D., Alves, C. F., Lopes, N., & Cruz-Cunha, M. M. (2022). A Systematic Review of Manufacturing Scheduling for the Industry 4.0. 1st International Symposium on Industrial Engineering and Automation (ISIEA 2022), Managing and Implementing the Digital Transformation, 21st-22nd June 2022, Bozen-Bolzano, Italy. Lecture Notes in Networks and Systems (pp. 237-249), Springer.
Wang, Q., Wang, X., Luo, H., & Xiong, J. (2020) An improved multi-objective evolutionary approach for aerospace shell production scheduling problem. Symmetry, 12(4), (DOI: 10.3390/sym12040509).
Wang, Z., Zhang, J., & Yang, S. (2019). An improved particle swarm optimization algorithm for dynamic job shop scheduling problems with random job arrivals. Swarm and Evolutionary Computation, 51, 100594, (DOI: 10.1016/j. swevo.2019.100594).
Wolpert, D. H., & Macready, W. G., (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82, (DOI: 10.1109/ 4235.585893).
Yapiz (2022a) Evolutionary clustering and automatic clustering. (URL: https://www.mathworks.com/matlabcentral/fileexchange/52865-evolutionary-clustering-and-automatic-clustering), retrieved February 2, 2022.
Yapiz (2022b) Multi-objective particle swarm optimization (MOPSO). (URL: https://www.mathworks.com/matlabcentral/fileexchange/52870-multi-objective-particle-swarm-optimization-mopso), retrieved February 2, 2022.
Zhang, J., Ding, G., Zou, Y., Qin, S., & Fu, J. (2019) Review of job shop scheduling research and its new perspectives under Industry 4.0. Journal of Intelligent Manufacturing, 30(4), 1809–1830, (DOI: 10.1007/s10845-017-1350-2).
Zhang, Q., & Li, H. (2007). MOEA/D: A multi-objective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712-731, (DOI: 10.1109/TEVC.2007.892759).
Zhang, S., Tang, F., Li, X., Liu, J., & Zhang, B. (2021). A hybrid multi-objective approach for real-time flexible production scheduling and rescheduling under dynamic environment in Industry 4.0 context. Computers & Operations Research, 132, 105267, (DOI: 10.1016/j.cor.2021.105267).
Zheng, F., Jin, K., Xu, Y., & Liu, M. (2022). Unrelated parallel machine scheduling with processing cost, machine eligibility and order splitting. Computers & Industrial Engineering, 171. (DOI: 10.1016/j.cie.2022.108483).