Abstract—This study aims at the availability optimization problem for n-stage standby system under different resource and design configuration constraints by applying Tabu-GA combination method. From the point of view of logistics engineering, availability optimization applied in the initial system development period, plays a key role to affect system reliability, system maintenance planning, logistics requirements, and related costs during system planned life cycle. In general, system availability can be improved by increasing component reliability level, component repair rate or the number of components in each subsystem. This proposed model combines Tabu method with Genetic Algorithm to solve system availability optimization problem. Through our method applied in the initial phase of system design and development, we can find the optimal allocation of component redundancy number, reliability and maintainability levels to minimize the total system cost under different configuration constraints such as weight, volume, and system availability requirements. The proposed numerical examples are demonstrated based on different system constraint requirements and parameter values. Through a simulation study of 30 times of calculations by applying this proposed combination method, stable results are clearly showed. Consequently, all numerical results and simulation studies clearly show that the proposed availability optimization model and Tabu-GA combination method developed in this paper can help solving system availability optimization problem in the initial system design and development period.
Index Terms—Availability, genetic algorithm, n-Stage standby system, tabu-GA combination method, tabu search.
Gia-Shie Liu is with Lunghwa University of Science and Technology, Taiwan, R.O.C. (e-mail: liugtw@yahoo.com.tw).
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Cite:Gia-Shie Liu, "Availability Optimization for Repairable n-Stage Standby System by Applying Tabu-GA Combination Method," International Journal of Modeling and Optimization vol. 3, no. 3, pp. 245-250, 2013.
