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Maximizing the Effectiveness of a Preventive Maintenance System: An Adaptive Modeling Approach

Author

Listed:
  • Mohan Gopalakrishnan

    (Department of Decision Sciences & MIS, Concordia University, 1455 de Maisonneuve Blvd. W, Montreal, PQ H3G 1M8, Canada)

  • Sanjay L. Ahire

    (Department of Management, Haworth College of Business, Western Michigan University, Kalamazoo, Michigan 49008)

  • David M. Miller

    (Department of Management Science and Statistics, Alabama Productivity Center, The University of Alabama, P.O. Box 870318, Tuscaloosa, Alabama 35487)

Abstract

The dynamic nature of an operating environment, such as machine utilization and breakdown frequency results in changing preventive maintenance (PM) needs for manufacturing equipment. In this paper, we present an approach to generate an adaptive PM schedule which maximizes the net savings from PM subject to workforce constraints. The approach consists of two components: (a) task prioritization based on a multi-logit regression model for each type of PM task, and (b) task rescheduling based on a binary integer programming (BIP) model with constraints on single-skilled and multi-skilled workforce availability. The task prioritization component develops a multi-logit regression for machine failure probability associated with each type of PM task at the beginning of the year, using historical data on machine utilization, PM, and machine breakdowns. At the start of each PM time-bucket (e.g., a month), we use the updated machine failure probability for each candidate PM task to compute its current contribution to net PM savings, which indicates its current priority. The task rescheduling BIP model incorporates the priorities in selecting tasks for the current bucket to maximize PM effectiveness subject to workforce availability, yielding an adaptive and effective PM schedule for each time-bucket of the master PM schedule. We examine the effect of using multi-skilled workforce on the overall PM effectiveness, and also provide an illustration from a newspaper publishing environment to explain the use of the approach. We have developed four heuristic algorithms to yield good solutions to large scale versions of this scheduling problem. The heuristics perform extremely well, and the best heuristic solution is within 1.4% of optimality on an average.

Suggested Citation

  • Mohan Gopalakrishnan & Sanjay L. Ahire & David M. Miller, 1997. "Maximizing the Effectiveness of a Preventive Maintenance System: An Adaptive Modeling Approach," Management Science, INFORMS, vol. 43(6), pages 827-840, June.
    • Handle: RePEc:inm:ormnsc:v:43:y:1997:i:6:p:827-840
    DOI: 10.1287/mnsc.43.6.827
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    Citations

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    Cited by:

    1. Yuqian Xu & Lingjiong Zhu & Michael Pinedo, 2020. "Operational Risk Management: A Stochastic Control Framework with Preventive and Corrective Controls," Operations Research, INFORMS, vol. 68(6), pages 1804-1825, November.
    2. Nima Safaei & Dragan Banjevic & Andrew Jardine, 2011. "Workforce-constrained maintenance scheduling for military aircraft fleet: a case study," Annals of Operations Research, Springer, vol. 186(1), pages 295-316, June.
    3. Rustogi, Kabir & Strusevich, Vitaly A., 2012. "Single machine scheduling with general positional deterioration and rate-modifying maintenance," Omega, Elsevier, vol. 40(6), pages 791-804.
    4. M. A. Kubzin & V. A. Strusevich, 2006. "Planning Machine Maintenance in Two-Machine Shop Scheduling," Operations Research, INFORMS, vol. 54(4), pages 789-800, August.
    5. Rebi Daldal & Özgür Özlük & Barış Selçuk & Zahed Shahmoradi & Tonguç Ünlüyurt, 2017. "Sequential testing in batches," Annals of Operations Research, Springer, vol. 253(1), pages 97-116, June.
    6. N Safaei & D Banjevic & A K S Jardine, 2011. "Bi-objective workforce-constrained maintenance scheduling: a case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1005-1018, June.

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