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Subject: search.filters.subject.case-based reasoning

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Now showing 1 - 10 of 11
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    A case-based reasoning approach to generating new product ideas
    (2006-08-01)
    Wu, Muh-Cherng
    ;
    Lo, Ying-Fu
    ;
    Hsu, Shang-Hwa
    ;
    工業工程與管理學系
    ;
    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
    ;
    Chu, C. N.
    ;
    Sun, Hung Min
    ;
    Chen, Ruey-Shun
    ;
    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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    A parallelized indexing method for large-scale case-based reasoning
    (2002-08-01)
    Chen, WC
    ;
    Tseng, SS
    ;
    Chang, LP
    ;
    Hong, TP
    ;
    Jiang, MF
    Case-based reasoning (CBR) is a problem-solving methodology commonly seen in artificial intelligence. It can correctly take advantage of the situations and methods in former cases to find out suitable solutions for new problems. CBR must accurately retrieve similar prior cases for getting a good performance. In the past, many researchers proposed useful technologies to handle this problem. However, the performance of retrieving similar cases may be greatly influenced by the number of cases. In this paper, the performance issue of large-scale CBR is discussed and a parallelized indexing architecture is then proposed for efficiently retrieving similar cases in large-scale CBR. Several algorithms for implementing the proposed architecture are also described. Some experiments are made and the results show the efficiency of the proposed method. (C) 2002 Elsevier Science Ltd. All rights reserved.
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    Product modelling to support case-based construction planning and scheduling
    (2004-05-01)
    Dzeng, RJ
    ;
    Tommelein, ID
    ;
    土木工程學系
    ;
    Department of Civil Engineering
    Many human schedulers create schedules by reusing past similar schedules. The retrieval and reuse of similar schedules are subjective and experience-based. This paper explores different notions of similarity required when performing different scheduling tasks. It describes the CasePlan system that helps schedulers retrieve and reuse parts of existing schedules based on a generic product model, and apply case-based reasoning to generate new schedules. The validation experiment demonstrated CasePlan's accuracy in determining individual subnetworks and activity durations, but weak performance in determining interlinks between subnetworks, which highly depends upon the availability of pertinent cases and the level of detail of project information. (C) 2004 Elsevier B.V. All rights reserved.
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    Critiquing contractors' scheduling by integrating rule-based and case-based reasoning
    (2004-09-01)
    Dzeng, RJ
    ;
    Lee, HY
    ;
    土木工程學系
    ;
    Department of Civil Engineering
    Nowadays, projects involve many activities. Hence, the review process requires the scheduler to be experienced both in field operations and in computer scheduling software. However, the dilemma faced by contractors is that many senior project managers are experienced in the field but not in computers, whereas many young engineers are skilled in computers but not in field operations. This work proposes a generalized framework to represent schedule knowledge, and a computer-based system that analyzes a project schedule and offers corrective advice on potential errors by integrating case-based and rule-based reasoning. (C) 2004 Elsevier B.V. All rights reserved.
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    Item type:Item,
    Learning heuristics for determining slurry wall panel lengths
    (2006-05-01)
    Dzeng, RJ
    ;
    Pan, NF
    ;
    土木工程學系
    ;
    Department of Civil Engineering
    Determining panel lengths for slurry walls is an engineering issue that involves complex geotechnical, design, and site considerations. In practice, the decision is made through a trial-and-error process. Relevant principles extracted from experts are not sufficiently detailed to generate a solution. This research proposes an inductive learning model for solving this problem. Given a new project whose panel lengths need to be determined, the model chooses similar cases from existing cases, based on case-based reasoning, performs an inductive learning, and uses correctness and coverage rates, and then static rules to verify the induced results. (c) 2005 Elsevier B.V. All rights reserved.
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    Item type:Item,
    A case-based reasoning approach to generating new product ideas
    (2006-08-01)
    Wu, Muh-Cherng
    ;
    Lo, Ying-Fu
    ;
    Hsu, Shang-Hwa
    ;
    工業工程與管理學系
    ;
    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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    Item type:Item,
    Development and evaluation of a tree-indexing approach to improve case-based reasoning: illustrated using the due date assignment problem
    (2006-08-01)
    Sha, D. Y.
    ;
    Liu, C. -H.
    ;
    工業工程與管理學系
    ;
    Department of Industrial Engineering and Management
    In this study a novel case indexing approach is proposed for case-based reasoning (CBR). This new approach, called the tree-indexing approach, is a modified form of the inductive learning-indexing (IL-indexing) approach and is especially applied to assist CBR in numeric prediction. The tree-indexing approach organizes the cases in the memory by inducting a tree-shaped structure, in order to improve the efficiency and effectiveness of case retrieval. The experiments, using three real world problems from the UCI repository, show that the CBR with the tree-indexing approach (T-CBR) is superior to the conventional CBR. This study also applies T-CBR for solving the due date assignment problem in a dynamic job shop environment in order to investigate whether T-CBR's expected benefits can be observed in practice. The results of the experiments show that our proposed T-CBR can indeed more accurately predict the job due date than the other methods presently in use.
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    Knowledge support for problem-solving in a production process: A hybrid of knowledge discovery and case-based reasoning
    (2007-07-01)
    Liu, Duen-Ren
    ;
    Ke, Chih-Kun
    ;
    資訊管理與財務金融系 註：原資管所+財金所
    ;
    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 the information needs of workers during the problem-solving process. Such processes are usually knowledge intensive tasks; therefore, workers need effective knowledge support that gives them the information necessary to identify the causes of a problem and enables them to take appropriate action to resolve the situation. In this paper, we propose a mining-based knowledge support system for problem-solving. In addition to adopting case-based reasoning to identify similar situations and the action taken to solve them, the proposed system employs text mining (information retrieval) techniques to extract the key concepts of situations and actions. These concepts form profiles that model workers' information needs when handling problems. Effective knowledge support can thus be facilitated by providing workers with situation/action-relevant information based on the profiles. Moreover, association rule mining is used to discover hidden knowledge patterns from historical problem-solving logs. The discovered patterns identify frequent associations between situations and actions, and can therefore provide decision-making knowledge, i.e., appropriate actions for handling specific situations. We develop a prototype system to demonstrate the effectiveness of providing situation/action relevant information and decision-making knowledge to help workers solve problems. (c) 2006 Published by Elsevier Ltd.
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    Item type:Item,
    A fuzzy CBR technique for generating product ideas
    (2008-01-01)
    Wu, Muh-Cherng
    ;
    Lo, Ying-Fu
    ;
    Hsu, Shang-Hwa
    ;
    工業工程與管理學系
    ;
    Department of Industrial Engineering and Management
    This paper 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 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 retrieved product ideas in order to obtain a higher ratio of valuable product ideas. Experiments indicate that the retrieving-and-filtering mechanism outperforms the prior retrieuing-only mechanism in terms of generating a higher ratio of valuable product ideas. (c) 2006 Elsevier Ltd. All rights reserved.