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

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 sub...

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Veröffentlicht in:Management science Jg. 43; H. 6; S. 827 - 840
Hauptverfasser: Gopalakrishnan, Mohan, Ahire, Sanjay L, Miller, David M
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hanover, MD., etc INFORMS 01.06.1997
Institute for Operations Research and the Management Sciences
Schriftenreihe:Management Science
Schlagworte:
Availability
binary integer programming
Breakdowns
Constraints
Cost control
Data
Decision making models
Effectiveness
Failure
Heuristic
Heuristics
Integer programming
Machinery
Maintenance costs
Management
Management science
Modeling
Modelling
multi-logit regression
Newspaper industry
Operations research
Optimal solutions
Planning
Preventive maintenance
Prioritization
Probability
Publishing
Publishing industry
Regression analysis
Scheduling
Skilled workers
Studies
Workforce
ISSN:0025-1909, 1526-5501
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Zusammenfassung: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.
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ISSN:0025-1909
1526-5501
DOI:10.1287/mnsc.43.6.827