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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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    A case-based reasoning approach to generating new product ideas
    (2006-08-01)
    Wu, Muh-Cherng
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    Lo, Ying-Fu
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    Hsu, Shang-Hwa
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    工業工程與管理學系
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    Department of Industrial Engineering and Management
    This paper presents an approach to facilitate the generation of new product ideas. In this approach, a product is represented by a vector, which involves a number of product attributes. These attributes are defined based on the user-centered design (UCD) paradigm. That is, the context of use and interactions with users for the product is described by these attributes. By the product representation, a product database can be established. For a benchmarking product, we use the case-based reasoning (CBR) technique to retrieve those products, which are "sufficiently relevant" to the benchmarking one, from the database. The functions of the retrieved products are the candidate new ideas to be added on the benchmarking product. These retrieved products are screened manually to identify creative and valuable ideas. A prototype system of this approach has been implemented. Experiments show that the proposed approach outperforms the brainstorming technique, which has been widely used in industry.
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    Using Case-Based Reasoning Method to Design a Return Merchandise Authorization System for Supply Chain Management in Internet of Thing
    (2017-01-01)
    Chen, Yeh-cheng
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    Chu, C. N.
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    Sun, Hung Min
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    Chen, Ruey-Shun
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    Tu, Mengru
    This paper drives into the deficiencies of the traditional RMA service system of a notebook computer manufacturer and analyzes the customer service behavior of its customer relationship management. It uses a global web-based framework and Java for development tool for the establishment of system. Data compilation procedure, with the help of the pro system case-based deduction technique, provided the system with an automatic analytical capacity, thereby further enhancing the capacity of the web-based intelligent RMA repair and maintenance system. The system allowed an interaction with the customers. The intelligent RMA system allowed us to improve repair and maintenance efficiency by 26% strongly boosting degree of customer satisfaction. Moreover, the global inventory management can reduce 12%; a boon that not only reduced the interest payments for inventory, but also eased up the company revolving fund cycle. The system can institute a more flexible part supply system, as well as enabled the Quality Assurance (QA) and R&D departments to understand the trouble-causing factors of the products that the company has sold. As a result, product AFR (Annual Failure Rate) dropped by around 85% Finally, we also used the repair and maintenance information to train new engineers. It saved us training time. Hence, the research has designed an innovative and practical IT (CRM) system.
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    Context-based knowledge support for problem-solving by rule-inference and case-based reasoning
    (2008)
    Liu, Duen-Ren
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    Ke, Chih-Kun
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    Wu, Mei-Yu
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    資訊管理與財務金融系 註：原資管所+財金所
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    Department of Information Management and Finance
    Problem-solving is an important process that enables corporations to create competitive business advantages. Traditionally, case-based reasoning techniques have been widely used to help workers solve problems. However, conventional approaches focus on identifying similar problems without exploring relevant context of problem situations. Situation features are generally occurred according to the context characteristics of problem. Moreover, situation features collected are usually partial or incomplete. Workers need to use knowledge inferred from relevant context information and previous problem-solving experience to clarify the causes and take appropriate action effectively. In this paper, we propose to use rule inference to infer possible situation features based on context information. Association rule mining is used to discover context-based inference rules from historical problem-solving logs. The discovered patterns identify frequent associations between context information and situation features, and therefore can be used to infer more situation features. By considering the inferred situation features, case-based reasoning can then be employed to identify similar situations effectively. Moreover, we employ information retrieval techniques to extract context-based situation profiles to model workers' information needs when handling problem situations in certain context. Effective knowledge support can thus be facilitated by providing workers with situ ation-releva nt information based on the profiles.