Using Grouping Genetic Algorithms to Design Preventive Maintenance System

• Main Authors: Yuan-Fu Yang, 楊元福
• Other Authors: Yu-Fang David Chiu
• Format: Others
• Language: en_US
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/25210616601351280185

碩士 === 中原大學 === 工業工程研究所 === 94 === As manufactured goods becoming more complex and customer’s expectations growing, increased attention is being paid to maintenance and improving product quality. To avoid the huge losses caused by sudden failures, the precision manufacturing of the Hi-Tech industry requires highly reliable and stable equipment. 　Enterprises have invested more and more resources to improve the rate of utilization of the machine in order to maintain the good operation of the process flow. Hence, it is important to develop a good method for preventive maintenance system design. The main purpose of this study is to design a preventive maintenance system, which is an optimal assignment of interchangeable component (OAIC) problem, a kind of redundancy allocation problem. Various heuristic methods and maintenance policies are discussed and summarized from the rapidly growing literature. Afterward, the grouping genetic algorithm (GGA) is presented for OAIC problem. GGA was first developed by Falkenauer in 1992 as a type of GA which exploits the special structure of grouping problem, and overcomes the drawbacks of GA. GGA can be effectively adopted for complex combinatorial problems, such as OAIC problem. In this study, the performance has been verified that the GGA is better than GA. The computational results show that GGA has more effectiveness in solving of OAIC problem.