{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/31815"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/31815","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"The Effect of Maintenance Policy on System Maintenance and System Life-Cycle Cost","abstract":"This research presents a framework system dynamics (simulation) model that evaluates the effect of maintenance policies on system performance and life-cycle cost. The model highlights factors such as learning, aging and the technological upgrades that occur during the life-cycle of a system. The metrics used to measure the effectiveness of maintenance policies are the system life-cycle cost and cumulative breakdowns. In this research, a varying maintenance policy has been modeled using system dynamics methodology to determine the future performance of the system that is dependent upon its past performance when breakdowns occur randomly. The main objective of this modeling approach is to balance the cost of preventive maintenance actions with the opportunity losses due to system breakdowns. The approach used in this research primarily involves forecasting future breakdowns using an average of accumulated opportunity losses. This research effort was mainly aimed at developing a (framework) model to determine effective maintenance policy for a system and evaluating the effect on the life-cycle cost for various scenarios. This model could further form the basis of a decision support system for maintenance modeling.","abstract_html":"This research presents a framework system dynamics (simulation) model that evaluates the effect of maintenance policies on system performance and life-cycle cost. The model highlights factors such as learning, aging and the technological upgrades that occur during the life-cycle of a system. The metrics used to measure the effectiveness of maintenance policies are the system life-cycle cost and cumulative breakdowns. In this research, a varying maintenance policy has been modeled using system dynamics methodology to determine the future performance of the system that is dependent upon its past performance when breakdowns occur randomly. The main objective of this modeling approach is to balance the cost of preventive maintenance actions with the opportunity losses due to system breakdowns. The approach used in this research primarily involves forecasting future breakdowns using an average of accumulated opportunity losses. This research effort was mainly aimed at developing a (framework) model to determine effective maintenance policy for a system and evaluating the effect on the life-cycle cost for various scenarios. 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