Abstract
A novel constraints model of credibility-fuzzy for the reliability redundancy allocation problem (RRAP) is studied in this work. The RRAP that must simultaneously decide reliability and redundancy of components is an effective approach in improving the system reliability. In practice various systems, the uncertainty condition of components used in the systems, which few studies have noticed this state over the years, is a concrete fact due to several reasons such as production conditions, different batches of raw materials, time reasons, and climatic factors. Therefore, this study adopts the fuzzy theory and credibility theory to solve the components uncertainty in the constraints of RRAP including cost, weight, and volume. Moreover, the simplified swarm optimization (SSO) algorithm has been adopted to solve the fuzzy constraints of RRAP. The effectiveness and performance of SSO algorithm have been experimented by four famous benchmarks of RRAP.
Highlights
The reliability enhancement has increasingly become important in most systems because the system reliability is an important measure of system performance in many practical systems
The simplified swarm optimization (SSO) algorithm, which was originally developed by Yeh in 2009 [35] and is a famous artificial intelligence, swarm algorithm and evolutionary algorithm, has been adopted to solve and optimize the proposed fuzzy constraints model of reliability redundancy allocation problem (RRAP) while aims to optimize the system reliability that the importance of enhancing system reliability as well as artificial intelligence, swarm algorithm and evolutionary algorithm can be proved by the related research by numerous literatures [38,39,40,41,42,43,44]
The system reliability and running time are obtained to compare the performance of the adopted SSO algorithm with particle swarm optimization (PSO), PSSO and genetic algoalgorithm (GA) for the four benchmarks of RRAP
Summary
Citation: Lin, H.C.-S.; Huang, C.-L.; Yeh, W.-C. A Novel Constraints and Wei-Chang Yeh 3 Department of Leisure and Recreation Administration, Ming Chuan University, Taoyuan 333, Taiwan Integration and Collaboration Laboratory, Department of Industrial Engineering and Engineering
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