Abstract
Job Shop Scheduling (JSS) problems emerge in many industrial sectors, where it is sought to maximize efficiency, minimize costs, minimize energy consumption among other conflicting objectives. Thus, these optimization problems involve two or more objectives. In recent years, new algorithms have been developed and proposed to tackle multi-objective problems such as the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Non-dominated Sorting Genetic Algorithm III (NSGA-III), among others. The main goal of this work is to compare the performance of these algorithms on solving bi-objective JSS problems on unrelated parallel machines with sequence-dependent setup times. For comparison purposes, the results of the hypervolume performance measure are statistically analysed. The results obtained show that the performance of these two algorithms is not significantly different and, therefore, NSGA-III does not represent a clear advantage on solving bi-objective JSS problems.
This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020, and EXPL/EME-SIS/1224/2021.
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dos Santos, F., Costa, L.A., Varela, L. (2024). Performance Comparison of NSGA-II and NSGA-III on Bi-objective Job Shop Scheduling Problems. In: Pereira, A.I., Mendes, A., Fernandes, F.P., Pacheco, M.F., Coelho, J.P., Lima, J. (eds) Optimization, Learning Algorithms and Applications. OL2A 2023. Communications in Computer and Information Science, vol 1981. Springer, Cham. https://doi.org/10.1007/978-3-031-53025-8_36
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