Application of Multi-Objective Genetic Algorithm to Quotation of Global Garment Companies

It is very important for companies in enhancing competitive advantages to reach quick response quote demands from customers, especially in the global competitive markets. The process of quoting is difficult and complex. The quote mechanism provided by this study could be separate into two parts. In the first part, when sellers get order demands and send demands to the operation office by Internet, users can reject unsuitable factories quickly for some important orders based on users’ experiences and limitation of orders. Then, users can use computers to allocate orders into suitable factories. After the interview with senior managers, there are two major operation objectives including “Minimum Cost” and “Minimum Make span”. For analyzing multi-objective planning problems, this study used Multi-Objective Genetic Algorithm (MOGA) to be the analytic tool. Based on the results, the mechanism can assist users to find some non-inferior solutions in only seconds. In addition, the results are quite comparable to those by Brute-Force Search. Therefore, this also explains that the results in this study process good predictive ability.