Condition-based maintenance policy optimization using genetic algorithms and Gaussian markov improvement algorithm

Michael Hoffman, Eunhye Song, Michael Brundage, Soundar Rajan Tirupatikumara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationPHM 2018 - 10th Annual Conference of the Prognostics and Health Management Society
EditorsMarcos Orchard, Anibal Bregon
PublisherPrognostics and Health Management Society
ISBN (Electronic)9781936263059
StatePublished - Aug 24 2018
Event10th Annual Conference of the Prognostics and Health Management Society, PHM 2018 - Philadelphia, United States
Duration: Sep 24 2018Sep 27 2018

Publication series

NameProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
ISSN (Print)2325-0178

Conference

Conference10th Annual Conference of the Prognostics and Health Management Society, PHM 2018
CountryUnited States
CityPhiladelphia
Period9/24/189/27/18

Fingerprint

Genetic algorithms
Maintenance
Preventive maintenance
Appointments and Schedules
Scheduling
Health
Availability
Costs and Cost Analysis
Monitoring
Costs

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Electrical and Electronic Engineering
  • Health Information Management
  • Computer Science Applications

Cite this

Hoffman, M., Song, E., Brundage, M., & Tirupatikumara, S. R. (2018). Condition-based maintenance policy optimization using genetic algorithms and Gaussian markov improvement algorithm. In M. Orchard, & A. Bregon (Eds.), PHM 2018 - 10th Annual Conference of the Prognostics and Health Management Society (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM). Prognostics and Health Management Society.
Hoffman, Michael ; Song, Eunhye ; Brundage, Michael ; Tirupatikumara, Soundar Rajan. / Condition-based maintenance policy optimization using genetic algorithms and Gaussian markov improvement algorithm. PHM 2018 - 10th Annual Conference of the Prognostics and Health Management Society. editor / Marcos Orchard ; Anibal Bregon. Prognostics and Health Management Society, 2018. (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM).
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Hoffman, M, Song, E, Brundage, M & Tirupatikumara, SR 2018, Condition-based maintenance policy optimization using genetic algorithms and Gaussian markov improvement algorithm. in M Orchard & A Bregon (eds), PHM 2018 - 10th Annual Conference of the Prognostics and Health Management Society. Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM, Prognostics and Health Management Society, 10th Annual Conference of the Prognostics and Health Management Society, PHM 2018, Philadelphia, United States, 9/24/18.

Condition-based maintenance policy optimization using genetic algorithms and Gaussian markov improvement algorithm. / Hoffman, Michael; Song, Eunhye; Brundage, Michael; Tirupatikumara, Soundar Rajan.

PHM 2018 - 10th Annual Conference of the Prognostics and Health Management Society. ed. / Marcos Orchard; Anibal Bregon. Prognostics and Health Management Society, 2018. (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Hoffman M, Song E, Brundage M, Tirupatikumara SR. Condition-based maintenance policy optimization using genetic algorithms and Gaussian markov improvement algorithm. In Orchard M, Bregon A, editors, PHM 2018 - 10th Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. 2018. (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM).