TY - GEN
T1 - Condition-based maintenance policy optimization using genetic algorithms and Gaussian markov improvement algorithm
AU - Hoffman, Michael
AU - Song, Eunhye
AU - Brundage, Michael
AU - Kumara, Soundar
N1 - Publisher Copyright:
© 2018 Prognostics and Health Management Society. All rights reserved.
PY - 2018/8/24
Y1 - 2018/8/24
N2 - Condition-based maintenance involves monitoring the degrading health of machines in a manufacturing system and scheduling maintenance to avoid costly unplanned failures. As compared with preventive maintenance, which maintains machines on a set schedule based on time or run time of a machine, condition-based maintenance attempts to minimize the number of times maintenance is performed on a machine while still attaining a prescribed level of availability. Condition-based methods save on maintenance costs and reduce unwanted downtime over its lifetime. Finding an analytically-optimal condition-based maintenance policy is difficult when the target system has non-uniform machines, stochastic maintenance time and capacity constraints on maintenance resources. In this work, we find an optimal condition-based maintenance policy for a serial manufacturing line using a genetic algorithm and the Gaussian Markov Improvement Algorithm, an optimization via simulation method for a stochastic problem with a discrete solution space. The effectiveness of these two algorithms will be compared. When a maintenance job (i.e., machine) is scheduled, it is placed in a queue that is serviced with either a first-in-first-out discipline or based on a priority. In the latter, we apply the concept of opportunistic window to identify a machine that has the largest potential to disrupt the production of the system and assign a high priority to the machine. A test case is presented to demonstrate this method and its improvement over traditional maintenance methods.
AB - Condition-based maintenance involves monitoring the degrading health of machines in a manufacturing system and scheduling maintenance to avoid costly unplanned failures. As compared with preventive maintenance, which maintains machines on a set schedule based on time or run time of a machine, condition-based maintenance attempts to minimize the number of times maintenance is performed on a machine while still attaining a prescribed level of availability. Condition-based methods save on maintenance costs and reduce unwanted downtime over its lifetime. Finding an analytically-optimal condition-based maintenance policy is difficult when the target system has non-uniform machines, stochastic maintenance time and capacity constraints on maintenance resources. In this work, we find an optimal condition-based maintenance policy for a serial manufacturing line using a genetic algorithm and the Gaussian Markov Improvement Algorithm, an optimization via simulation method for a stochastic problem with a discrete solution space. The effectiveness of these two algorithms will be compared. When a maintenance job (i.e., machine) is scheduled, it is placed in a queue that is serviced with either a first-in-first-out discipline or based on a priority. In the latter, we apply the concept of opportunistic window to identify a machine that has the largest potential to disrupt the production of the system and assign a high priority to the machine. A test case is presented to demonstrate this method and its improvement over traditional maintenance methods.
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M3 - Conference contribution
AN - SCOPUS:85071477089
T3 - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
BT - PHM 2018 - 10th Annual Conference of the Prognostics and Health Management Society
A2 - Bregon, Anibal
A2 - Orchard, Marcos
PB - Prognostics and Health Management Society
T2 - 10th Annual Conference of the Prognostics and Health Management Society, PHM 2018
Y2 - 24 September 2018 through 27 September 2018
ER -