A multi-objective selective maintenance optimization method for series-parallel systems using NSGA-III and NSGA-II evolutionary algorithmsXu, E. B. (Avtor) Yang, M. S. (Avtor) Li, Y. (Avtor) Gao, X. Q. (Avtor) Wang, Z. Y. (Avtor) Ren, L. J. (Avtor) maintenanceseries-parallel systemmaintenance decision modelmulti-objective optimizationselective maintenanceevolutionary algorithmsnon-dominated sorting genetic algorithmNSGA-IINSGA-IIIAiming at the problem that the downtime is simply assumed to be constant and the limited resources are not considered in the current selective maintenance of the series-parallel system, a three-objective selective maintenance model for the series-parallel system is established to minimize the maintenance cost, maximize the probability of completing the next task and minimize the downtime. The maintenance decision-making model and personnel allocation model are combined to make decisions on the optimal length of each equipment’s rest period, the equipment to be maintained during the rest period and the maintenance level. For the multi-objective model established, the NSGA-III algorithm is designed to solve the model. Comparing with the NSGA-II algorithm that only considers the first two objectives, it is verified that the designed multi-objective model can effectively reduce the downtime of the system.Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering20212026-03-04 08:04:35Članek v reviji97404slContent from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.