Rough set based approach to supply chain modeling

• Main Authors: Yi-Chun Lin, 林宜君
• Other Authors: Chun-Che Huang
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/20074691994222346780

碩士 === 國立暨南國際大學 === 資訊管理學系 === 95 === In recent years, supply chain management (SCM) has been touted as one of the major strategies of improving organizational performance and generating a competitive advantage. A growing number of firms have begun to realize the strategic importance of planning, controlling, and designing a supply chain as a whole. In an effort to help firms capture the spirit of integration and coordination across the supply chain and to subsequently make better supply chain decisions, (i) synthesize of past supply chain modeling efforts and (ii) a novel approach to modeling supply chain are required. This paper develops (i) a rough set based approach to reduce the complexity of data space and induct decision rules and (ii) the generic label correcting algorithm (GLC) incorporated with the decision rules to solve various supply chain modeling problems. (iii) an incremental technique reduce the time and complex of the data set with new added data. This proposed approach is agile because by combining various operators and comparators, different types of paths in the reduced networks can be solved with one algorithm. Furthermore, the cases, e.g., the supplier selection and location selection problems are illustrated.