Application of Self-Adaptive Genetic Algorithm to Decision Support Systems for Global Garment Production

• Main Authors: Shiue-Shiun Li, 李學勛
• Other Authors: Rong-Chang Chen
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/01669473008872564318

碩士 === 國立臺中技術學院 === 事業經營研究所 === 94 === It is very important for managers to make correct production decisions quickly in enhancing competitive capabilities. In the real companies, decision makers or senior managers only have few minutes for making production decisions. There are excess and complex influencing factors when senior managers make decisions. If senior managers have to make decisions with the influencing factors by their own experiences, some influencing factors would be omitted. Sometimes, it causes some mistakes and it also increases production cost. Because many global companies have limited profits and they can not cover extra cost, they need a useful tool to assist senior managers in order allocation into suitable factories within short time. This study arms to develop a global decision support system for garment production. Some past studies presented that using traditional genetic algorithm (or simple genetic algorithm) could analyze data within complex and excess influencing factors. However, there are some drawbacks in traditional genetic algorithm. The results created by traditional genetic algorithm are usually unstable, and it is impossible for managers to adjust genetic parameters. Thus, we used self-adaptive genetic algorithm as the analytic tool of global decision support system. Results from this study show that the global decision support system which used self-adaptive genetic algorithm as then analytic tool can get suitable decisions for the garment industry. Visual charts are used to present results, and they enable users to understand situations of orders easily. Characteristics of this system are easy to use, effective, and useful.