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

Michael Hoffman, Eunhye Song, Michael Brundage, Soundar Kumara

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

3 Scopus citations

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
EditorsAnibal Bregon, Marcos Orchard
PublisherPrognostics and Health Management Society
ISBN (Electronic)9781936263295
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

All Science Journal Classification (ASJC) codes

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

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  • Cite this

    Hoffman, M., Song, E., Brundage, M., & Kumara, S. (2018). Condition-based maintenance policy optimization using genetic algorithms and Gaussian markov improvement algorithm. In A. Bregon, & M. Orchard (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.