近年來3C(Computer, Communication, Consumer Electronics)製造產業在台灣蓬勃發展,隨著地球村的概念形成,使得產品可以銷售到世界各地。廠商為了提供較好的售後服務,勢必要提供產品退貨維修(Return Merchandise Authorization, RMA)的管道,因此維修中心(Service Center, SC)的需求就因應而生。也因為維修中心的設置對於整體供應鏈體系扮演著非常重要的角色,因此設置正確的維修中心位置及適合的數量,將可以減低營運成本及增加服務水準。 本研究以總成本最小化為目標,成本考量包括維修中心的設置成本、存貨成本、運輸成本、維修成本及處罰成本,並利用基因演算法(Genetic Algorithms, GA)作為演算工具來決定維修中心的位置及數量。由於本研究是屬於全籌運籌(Global Logistics, GL)中典型的區域設置(Location Determination, LD)問題,因此在研究中先利用整數規劃建構區位設置問題,接著利用基因演算法來決定設置位置及數量,最後針對前面所建立的模型,利用某家主機板廠商的實際數據作驗證,實驗結果顯示基因演算法應用於區位設置問題有明顯的績效。
The manufacture of 3C (Computer, Communication, and Consumer Electronics) products has been growing rapidly in Taiwan. These products are supplied to customers globally. In order to provide better after-sales service, the manufacturer needs to operate service centers (SC) to manage Return Merchandise Authorization (RMA) for product maintenance and repair. The geographic locations of these SC play an important role in RMA, as the right amount of SC at the right locations can reduce the operating cost and increase service level. This paper uses Genetic Algorithms (GA) to determine the best number of SC and their locations to minimize the total cost including SC setup cost, inventory cost, transportation cost, repair cost, and penalty cost. The problem studied in this thesis is a typical Location Determination (LD) problem of Global Logistics (GL). An integer programming mathematical model is first used to describe the LD problem, and then GA is used to determine the best number of SC and their locations. Real data of a motherboard manufacturer are used to evaluate the performance of the proposed approach. Experimental results show that GA is effective in solving LD problems.
為了持續優化網站功能與使用者體驗,本網站將Cookies分析技術用於網站營運、分析和個人化服務之目的。
若您繼續瀏覽本網站,即表示您同意本網站使用Cookies。