System reliability-redundancy optimization with cold-standby strategy by fitness-distance balance stochastic fractal search algorithm

Abstract

Reliability-redundancy allocation problem (RRAP) is an interesting subject in the field of reliability engineering that attracted attention of many researchers. RRAP tries to maximize the system reliability while creating a tradeoff between the component reliability and level of redundancy for each subsystem. Early studies in the cold-standby strategy used the lower bound formula to estimate the system reliability. But in this paper, a newly introduced Markovian process-based approach is applied for calculating the exact reliability values of cold-standby systems with the imperfect switching system. A newly developed evolutionary algorithm called fitness-distance balance stochastic fractal search is adjusted for solving the RRAP as an NP-hard optimization model, and the obtained results are compared with other counterparts by using numerical examples on three well-known benchmark problems. Finally, to justify the performance of the applied Markovian method in practical viewpoint, a pump system with non-identical components in a chemical plant is analysed as a real-world case study.

KEYWORDS:

• Reliability optimization
• reliability-redundancy allocation problem
• cold-standby strategy
• continuous time Markov chain
• meta-heuristic algorithm

Disclosure statement

No potential conflict of interest was reported by the author(s).