| Summary: | 博士 === 國立交通大學 === 工業工程與管理系所 === 95 === This dissertation presents a fuzzy CBR (case-based reasoning) technique for generating new product ideas from a product database for enhancing the functions of a given product (called the baseline product). In the database, a product is modeled by a 100-attribute vector, 87 of which are used to model the use-scenario and 13 are used to describe the manufacturing/recycling features. Based on the use-scenario attributes and their relative weights—determined by a fuzzy AHP (Analytic Hierarchy Process) technique, a fuzzy CBR retrieving mechanism is developed to retrieve product-ideas that tend to enhance the functions of the baseline product. Based on the manufacturing/recycling features, a fuzzy CBR mechanism is developed to screen the incompatible products ideas with baseline product in order to obtain a higher ratio of valuable product ideas. A prototype system of the proposed approach, called PIAS (product innovation aiding system), has been implemented. Experiments show that the PIAS outperforms the brainstorming approach in generating creative and valuable product ideas. In addition, experiments indicate that the retrieving-and-filtering mechanism outperforms the retrieving-only mechanism in terms of generating a higher ratio of valuable product ideas.
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