Using Fuzzy Cognitive Map for RFID-based Reverse Logistic Model Decision Support and Application

• Main Author: 吳昌儒
• Other Authors: Amy J. C. Trappey
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/15160061135557533679

碩士 === 國立清華大學 === 工業工程與工程管理學系 === 97 === With increasing environmental consciousness, green directives (e.g., WEEE) and liberal return policies, recycling, re-use, re-assembly of materials, components and products are attracting more attention from manufacturers and the public. Consequently, reverse logistics research is being used to analyze the processes associated with the flows of products, components and materials from end users to re-users (e.g., second markets or landfills). The components may be widely dispersed during reverse logistics, which makes it difficult to efficiently collect, re-use and re-assemble disposed components for reprocessing and remanufacturing. As a result, Radio Frequency Identification (RFID) technology combined with EPCglobal Network architecture is applied to enable efficient product and component data collection and data transmission. The proposed information system uses RFID for real time data tracking and the EPCglobal network architecture defines the layers (RFID tags, RFID readers, EPC middleware, EPC information service, Object naming service, and EPC discovery service) for data transmission. This research develops a decision support model which integrates fuzzy cognitive maps and genetic algorithm. The major advantage of using cognitive maps is that the model and the relationships among nodes (states) are linguistically expressed both quantitatively and qualitatively. Combining the model and the information system, and integrating the EPCglobal network architecture and RFID technology, the goals of the research are achieved. Inference analysis contributes to the system response forecasting and decision analysis supports the manager make a stable reverse logistics system by adjusting some internal operation factors. This research provides a more comprehensive view of the supply chain, and achieves rapid and effective decision-making support, to enhance competitiveness and efficiency of the supply chain.