An integrated strategy for efficient business plan and maintenance plan for systems with a dynamic failure distribution

• Published in: Journal of intelligent manufacturing Vol. 24; no. 1; pp. 87 - 97
• Main Authors: Schutz, J., Rezg, N., Léger, J.-B.
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.02.2013; Springer Nature B.V; Springer Verlag (Germany)
• Subjects: Analysis; Business; Business and Management; Business plans; Computer Science; Control; Dynamical systems; Dynamics; Failure; Genetic algorithms; Machines; Maintenance; Maintenance costs; Maintenance management; Manufacturing; Manufacturing execution systems; Mathematical models; Maximum likelihood method; Mechatronics; Missions; Modeling and Simulation; Operations Research; Optimization; Planning; Policies; Preventive maintenance; Processes; Production; Robotics; Studies; Finite horizon; Sequential maintenance; Dynamic failure law; Genetic algorithms
• ISSN: 0956-5515; 1572-8145
• DOI: 10.1007/s10845-011-0543-3

The purpose of this study is to propose an integrated strategy to determine jointly efficient business and maintenance plans. The studied system is subject to random failures with a dynamic failure law. It must perform a set of missions (among M possible missions) over a finite planning horizon. Each mission may have different characteristics that depend on operational and environmental conditions. The determination of a business plan consists in choosing and scheduling the missions to be performed. To maximize the net profit (profits generated by the achievement of missions minus maintenance costs), two meta-heuristics based on genetic algorithms are developed. The first genetic algorithm is used to determine the business plan and the second one generates an efficient maintenance plan. Two maintenance policies are studied: a minimalist policy which involves only corrective maintenance actions and another policy, called sequential, which involves several imperfect preventive maintenance activities performed at predetermined times. Two cases are studied for the latter strategy. The first one considers the maintenance effectiveness factor as being the same for all preventive maintenance actions and we search for the best factor. In the second case, we consider maintenance actions with different efficiency factors and we look for the optimal value of each factor. Finally, a numerical example illustrates the proposed approach and the difference between the maintenance policies.