• 산업공학
• /
• 제21권3호
• /
• pp.254-261
• /
• 2008

Connected-(r,s)-out of-(m,n) : F system is an important topic in redundancy design of the complex system reliability and it's maintenance policy. Previous studies applied Monte Carlo simulation and genetic, simulated annealing algorithms to tackle the difficulty of maintenance policy problem. These algorithms suggested most suitable maintenance cycle to optimize maintenance pattern of connected-(r,s)-out of-(m,n) : F system. However, genetic algorithm is required long execution time relatively and simulated annealing has improved computational time but rather poor solutions. In this paper, we propose the ant colony optimization approach for connected-(r,s)-out of-(m,n) : F system that determines maintenance cycle and minimum unit cost. Computational results prove that ant colony optimization algorithm is superior to genetic algorithm, simulated annealing and tabu search in both execution time and quality of solution.

• 대한산업공학회지
• /
• 제30권3호
• /
• pp.250-260
• /
• 2004

This study considers a linear connected-(r,s)-out-of-(m,n):F lattice system whose components are ordered like the elements of a linear (m,n )-matrix. We assume that all components are in the state 1 (operating) or 0 (failed) and identical and s-independent. The system fails whenever at least one connected (r,s)-submatrix of failed components occurs. The purpose of this paper is to present an optimization scheme that aims at minimizing the expected cost per unit time. To find the optimal threshold of maintenance intervention, we use a genetic algorithm for the cost optimization procedure. The expected cost per unit time is obtained by Monte Carlo simulation. The sensitivity analysis to the different cost parameters has also been made.

• 대한산업공학회지
• /
• 제34권1호
• /
• pp.98-107
• /
• 2008

The purpose of this paper is to present an optimization scheme that aims at minimizing the expected cost per unittime. This study considers a linear connected-(r, s)-ouI-of-(m, n):f lattice system whose components are orderedlike the elements of a linear (m, n)-matrix. We assume that all components are in the state 1 (operating) or 0(failed) and identical and s-independent. The system fails whenever at least one connected (r, s)-submatrix offailed components occurs. To find the optimal threshold of maintenance intervention, we use a simulatedannealing(SA) algorithm for the cost optimization procedure. The expected cost per unit time is obtained byMonte Carlo simulation. We also has made sensitivity analysis to the different cost parameters. In this study,utility maintenance model is constructed so that minimize the expense under full equipment policy throughcomparison for the full equipment policy and preventive maintenance policy. The full equipment cycle and unitcost rate are acquired by simulated annealing algorithm. The SA algorithm is appeared to converge fast inmulti-component system that is suitable to optimization decision problem.

• 한국경영과학회:학술대회논문집
• /
• 한국경영과학회 2006년도 추계학술대회
• /
• pp.618-629
• /
• 2006

This Paper is about optimizing preventive maintenance period of connected (r,s) out of(m,n) : F lattice system that one of multi-component system, (m,n) matrix failure of whole system is occurrence when parts that belong in (r,s) matrix part procession of parts arranged with procession are breakdown all. The preventive maintenance about system is very important viewing from system reliability and operational expense viewpoint. Preventive maintenance that misses a time calls big loss by system failure and expense of frequent full equipment is paid excessively in preventive maintenance itself but expense is paid much in preventive maintenance itself and whole expense escalation can be achieved preferably. Through this research, reliability model is constructed that do expense by smallest under full equipment policy chosen through comparison of each full equipment policy and preventive maintenance expense full equipment cycle and r ,s value are made using simulated annealing algorithm and simulated annealing algorithm that converge fast in multi-component system certified most suitable to optimization decision