Application of Multi-Objective Genetic Algorithm to Quote in Global Garment Industry

• Main Authors: Ying-Hua Chen, 陳盈樺
• Other Authors: Rong-Chang Chen
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/90647370925693240248

碩士 === 國立臺中技術學院 === 事業經營研究所 === 95 === In global markets, it is very crucial for companies to enhance competitive advantages to achieve quick response quote demand from customers. The quoting process is difficult and complex. The traditional methods in solving this problem was that decisions were always made by senior managers’ own experiences in garment industry. Some mistakes may be made while the decisions are made in little time. Thus, some scholars suggested Genetic Algorithm (GA), which was adopted to analyze orders and give suitable results, to assist companies in decision making. However, this method may not be suitable for solving real situations since previous research employed single-objective planning problem to present real situations, whereas many of real situations are multi-objective planning problems. Hence, the aim of this study is to propose a quote mechanism and find the suitable analytic tool for multi-objective planning problems. The quote mechanism proposed by this study can be separated into two parts. First, when receiving order demands and sending demands to the operation office through Internet. In terms of own experiences and limitation of orders, unsuitable factories can be eliminated quickly for some important orders. Those orders are then allocated into suitable factories by computers. After interviewing with senior managers, there are two main operation objectives including “Minimum Cost” and “Minimum Makespan”. To analyze multi-objective planning problems, Multi-Objective Genetic Algorithm (MOGA) was adopted to achieve the main purposes of this study. The results indicate that the mechanism can assist users to find some non-inferior solutions in few seconds. In addition, the results are quite comparable to those by Brute-Force Search. Furthermore, this also explains that the results yielding from this study possess good predictive ability.