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수평보급이 적용된 Multi Indenture Multi Echelon 시스템에 대한 시뮬레이션 설계
정일한,윤원영 대한산업공학회 2008 산업공학 Vol.21 No.4
This paper deals with a design problem of simulation for MIME (multi indenture and multi echelon) with lateral transshipment. Especially, we consider lateral transshipments in case that (S-1, S) ordering policy is used in multi echelon repair system. Some rules for ordering spare parts in lateral transshipments between the lowest-level units are studied and are implemented by an activity diagram in object-oriented method. By numerical examples, we compare regular (S-1, S) ordering policy and (S-1, S) policy with lateral transshipment.
무기체계 RAM 시뮬레이션의 정확도 향상을 위한 요소별 영향 분석
정일한,박삼준,Chung, Il-Han,Park, Sam-Joon 한국군사과학기술학회 2008 한국군사과학기술학회지 Vol.11 No.6
In the development stage of weapon system, it is important to analyze RAM(Reliability, Availability and Maintainability) characteristics. RAM simulation is one of the advanced techniques for analyzing RAM to overpass the limit of mathematical techniques. However, it is necessary to obtain correct input data for reliability and maintainability about target and support system to get highly accurate results through RAM simulation. In this study, we propose the technical method to improve the results by defining input data of simulation more correctly based on analyzing effects of RAM characteristics by major factors.
대안 부품을 고려한 다계층 시스템의 중복 할당을 위한 입자 군집 최적화
정일한 한국품질경영학회 2019 품질경영학회지 Vol.47 No.4
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.