Optimum preventive maintenance policies for systems subject to random working times, replacement, and minimal repair

• Published in: Computers & industrial engineering Vol. 67; pp. 185 - 194
• Main Author: Chang, Chin-Chih
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.01.2014; Pergamon Press Inc
• Subjects: Minimal repair; Operations research; Optimization; Optimization techniques; Preventive maintenance; Production scheduling; Random variables; Random working time; Reliability; Replacement; Studies; Working hours; Replacement; Reliability; Preventive maintenance; Optimization; Minimal repair; Random working time
• DOI: 10.1016/j.cie.2013.11.011
• ISSN: 0360-8352

•We analyze the preventive maintenance policy for systems with random working times.•The existence and uniqueness properties of optimal preventive maintenance policy are shown.•Imperfect maintenance is a stochastic choice between replacement and minimal repair.•The model provides a general framework for analyzing the maintenance policies. This paper proposes, from the economical viewpoint of preventive maintenance in reliability theory, several preventive maintenance policies for an operating system that works for jobs at random times and is imperfectly maintained upon failure. As a failure occurs, the system suffers one of two types of failure based on a specific random mechanism: type-I (repairable) failure is rectified by a minimal repair, and type-II (non-repairable) failure is removed by a corrective replacement. First, a modified random and age replacement policy is considered in which the system is replaced at a planned time T, at a random working time, or at the first type-II failure, whichever occurs first. Next, as one extended model, the system may work continuously for N jobs with random working times. Finally, as another extended model, we might consider replacing an operating system at the first working time completion over a planned time T. For each policy, the optimal schedule of preventive replacement that minimizes the mean cost rate is presented analytically and discussed numerically. Because the framework and analysis are general, the proposed models extend several existing results.