Joint redundancy and imperfect preventive maintenance optimization for series–parallel multi-state degraded systems

• Published in: Reliability engineering & system safety Vol. 103; pp. 51 - 60
• Main Authors: Nourelfath, Mustapha, Châtelet, Eric, Nahas, Nabil
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.07.2012; Elsevier
• Subjects: Applied sciences; Computer Science; Degradation; Exact sciences and technology; Industrial metrology. Testing; Maintenance optimization; Mechanical engineering. Machine design; Meta-heuristics; Multi-state systems; Operational research and scientific management; Operational research. Management science; Redundancy optimization; Reliability theory. Replacement problems; Series–parallel systems; Systems and Control; RAP; Multi-state systems; Maintenance optimization; Series–parallel systems; MSS; Meta-heuristics; UMGF; Degradation; Redundancy optimization; GA; PM; SP; TS; Markov process; State space; Series system; Modeling; Optimization; Vector space; Partitioning; Tabu search; Parallel system; Defect; Cost function; Budget; Metamodel; Availability; Generating function; Dependability; Redundancy; Load curve; Maintenance; Series-parallel systems; Preventive maintenance; Genetic algorithm; Heuristic method; Planning; Multiple choice; Reliability
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2012.03.004

This paper formulates a joint redundancy and imperfect preventive maintenance planning optimization model for series–parallel multi-state degraded systems. Non identical multi-state components can be used in parallel to improve the system availability by providing redundancy in subsystems. Multiple component choices are available in the market for each subsystem. The status of each component is considered to degrade with use. The objective is to determine jointly the maximal-availability series–parallel system structure and the appropriate preventive maintenance actions, subject to a budget constraint. System availability is defined as the ability to satisfy consumer demand that is represented as a piecewise cumulative load curve. A procedure is used, based on Markov processes and universal moment generating function, to evaluate the multi-state system availability and the cost function. A heuristic approach is also proposed to solve the formulated problem. This heuristic is based on a combination of space partitioning, genetic algorithms (GA) and tabu search (TS). After dividing the search space into a set of disjoint subsets, this approach uses GA to select the subspaces, and applies TS to each selected sub-space.