Integration of Association Rules and aiNET-PSO Methods for Supplier Order Quantity Allocation

• Main Authors: Chun-meng Bai, 白淳盟
• Other Authors: Ren-jieh Kuo
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/39214540937401548737

碩士 === 國立臺灣科技大學 === 工業管理系 === 99 === Due to global competition, most of the enterprises focus both on accelerating the implementation efficiency and minimizing the operation costs. One of the ways to achieve the above goals is outsourcing. Thus, supplier selection has become the most critical factor for achieving competitive advantage. This study first intends to employ one of the association rule mining techniques, TD-FP-growth algorithm, to prune off unimportant suppliers from the existing suppliers. This can provide us the key suppliers. Then, an integrated optimization artificial immune network (Opt-aiNET) and particle swarm optimization (PSO) is proposed to allocate the orders for the key suppliers with minimum cost. In order to verify the proposed methods, a case company’s daily purchasing ledger focusing on the consumer electronic product manufacturers is applied. TD-FP-growth algorithm is able to select the key suppliers. Besides, the proposed method, integrated Opt-aiNET and PSO really can provide lowest cost compared to those of genetic algorithm, PSO, and Opt-aiNET.