대안 부품을 고려한 다계층 시스템의 중복 할당을 위한 입자 군집 최적화
Particle Swarm Optimization for Redundancy Allocation of Multi-level System considering Alternative Units
1
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
Purpose: The problem of optimizing redundancy allocation in multi-level systems is considered when each item in a multi-level system has alternative items with the same function. The number of redundancy of multi-level system is allocated to maximize the reliability of the system under path set and cost limitation constraints. Methods: Based on cost limitation and path set constraints, a mathematical model is established to maximize system reliability. Particle swarm optimization is employed for redundant allocation and verified by numerical experiments. Results: Comparing the particle swarm optimization method and the memetic algorithm for the 3 and 4 level systems, the particle swarm optimization method showed better performance for solution quality and search time. Particularly, the particle swarm optimization showed much less than the memetic algorithm for variation of results. Conclusion: The proposed particle swarm optimization considerably shortens the time to search for a feasible solution in MRAP with path set constraints. PS optimization is expected to reduce search time and propose the better solution for various problems related to MRAP.
Abstract
Purpose: The problem of optimizing redundancy allocation in multi-level systems is considered when each item in a multi-level system has alternative items with the same function. The number of redundancy of multi-level system is allocated to maximize the reliability of the system under path set and cost limitation constraints. Methods: Based on cost limitation and path set constraints, a mathematical model is established to maximize system reliability. Particle swarm optimization is employed for redundant allocation and verified by numerical experiments. Results: Comparing the particle swarm optimization method and the memetic algorithm for the 3 and 4 level systems, the particle swarm optimization method showed better performance for solution quality and search time. Particularly, the particle swarm optimization showed much less than the memetic algorithm for variation of results. Conclusion: The proposed particle swarm optimization considerably shortens the time to search for a feasible solution in MRAP with path set constraints. PS optimization is expected to reduce search time and propose the better solution for various problems related to MRAP.
참고문헌 (9)
- 정일한, "대안 부품을 고려한 다계층 시스템의 최적 중복 설계", 품질경영학회지, 43(1), pp.31~42 (2015), 한국품질경영학회[DOI]
- Fyffe, D. E., "System reliability allocation and a computational algorithm", IEEE Trans. Reliability, 17(2), pp.64~69 (1968)
- Kennedy, J., "Particle swarm optimization", Proceedings of IEEE international conference on neural networks, pp.1942~1948 (1995)
- Kumar, R., "Optimal multilevel redundancy allocation in series and series-parallel systems", Computers and Industrial Engineering, 57, pp.169~180 (2009)
- Yeh, W. C, "A two-stage discrete particle swarm optimization for the problem of multiplemulti-level redundancy allocation in series systems", Expert Systems with Applications, 36(5), pp.9192~9200 (2009)
- Yeh, W. C., "Simplified swarm optimization with modular search for the general multi-level redundancy allocation problem in series-parallel systems", 2016 IEEE Congress on Evolutionary Computation (CEC) Evolutionary Computation (CEC), pp.778~784 (2016)
- Yun, W. Y., "Multi-level redundancy optimization in series systems", Computers and Industrial Engineering, 46, pp.337~346 (2004)
- Yun, W. Y., "Multiple multi-level redundancy allocation in series systems", Reliability Engineering and System Safety, 92, pp.308~313 (2007)
- 윤원영, "다수준 시스템의 중복 할당 최적화 문제", 대한산업공학회지, 43(2), pp.135~146 (2017), 대한산업공학회[DOI]