Selective maintenance for maximising system availability: a simulation approach. (2017)

• Record Type: Journal Article
• Authors: Cao, Wenbin; Jia, Xisheng; Hu, Qiwei; Song, Wenyuan; Ge, Hongyu
• Is Part Of: International journal of innovative computing and applications. Volume 8:Number 1(2017)
• Page Start: 12
• Page End: 20
• Publication Date: 2017
• Subjects: selective maintenance -- system availability -- Monte Carlo simulation -- genetic algorithms; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Selective maintenance is a process of identifying the sets of components to be repaired and corresponding maintenance activities to be performed when given sets of limited maintenance resources, such as time, budget, repairman, spares, etc. Examples of selective maintenance abound in applications such as manufacturing systems, military systems, power generation systems, etc. In this paper, a simulation method is proposed to optimally select the maintenance schemes, including the selected components to be repaired and maintenance tasks allocation with the objective of maximising system availability. Genetic algorithm (GA) is adopted to optimally allocate maintenance tasks to the limited repairman. An illustrative example is presented to demonstrate the applicability. Furthermore, the effects of repairman and mission duration on system availability are discussed in the end.