Eryilmaz, SerkanBozbulut, Ali RizaIndustrial Engineering2024-07-052024-07-052014350951-83201879-083610.1016/j.ress.2014.06.0172-s2.0-84905564720https://doi.org/10.1016/j.ress.2014.06.017https://hdl.handle.net/20.500.14411/191Eryilmaz, Serkan/0000-0002-2108-1781In this paper, we study a multi-state weighted k-out-of-n:G system model in a dynamic setup. In particular, we study the random time spent by the system with a minimum performance level of k. Our method is based on ordering the lifetimes of the system's components in different state subsets. Using this ordering along with the Monte-Carlo simulation algorithm, we obtain estimates of the mean and survival function of the time spent by the system in state k or above. We present illustrative computational results when the degradation in the components follows a Markov process. (C) 2014 Elsevier Ltd. All rights reserved.eninfo:eu-repo/semantics/closedAccessMonte-Carlo simulationMulti-state weighted k-out-of-n:G systemSurvival functionArticleQ11316165WOS:000341466500006