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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Li, HLHas files: No

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    Comments on "Fuzzy programming with nonlinear membership functions..."
    (1999-01-01)
    Li, HL
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    Yu, CS
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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
    Yang ct al., in their paper "Fuzzy programming with nonlinear membership functions...", published in Fuzzy Sets and Systems 41 (1991), declared that their model can solve a fuzzy program with an S-shaped membership function by adding only one 0-1 variable. This paper indicates that their declaration is correct only for a specific type of S-shape membership functions. We propose another model to treat the fuzzy programs which cannot be solved effectively by Yang et al. (C) 1999 Elsevier Science B.V. All rights reserved.
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    An efficient method for solving linear goal programming problems
    (1996-08-01)
    Li, HL
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    資訊管理與財務金融系 註：原資管所+財金所
    ;
    Department of Information Management and Finance
    This note proposes a solution algorithm for linear goal programming problems. The proposed method simplifies the traditional solution methods. Also, the proposed method is computationally efficient.
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    Incorporating competence sets of decision makers by deduction graphs
    (1999-03-01)
    Li, HL
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    交大名義發表
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    National Chiao Tung University
    This paper proposes an optimization model of incorporating competence sets of group decision makers to maximize the total benefit of the whole group. Such an incorporation model is formulated as finding a deduction graph linked from the nodes of existing competencies to the nodes of desired competencies. Compared with other methods treating competence set problems (Yu and Zhang 1991, Li and Yu 1994, and Shi and Yu 1996), the proposed model can solve problems involving multiple decision makers; in addition it allows the network to be cyclic and to contain compound nodes.
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    A distributed global optimization method for packing problems
    (2003-04-01)
    Li, HL
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    Tsai, JF
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    Hu, NZ
    ;
    資訊管理與財務金融系 註：原資管所+財金所
    ;
    Department of Information Management and Finance
    Packing optimization problems aim to seek the best way of placing a given set of rectangular cartons within a minimum volume rectangular container. Currently, packing optimization methods either have difficulty in finding a globally optimal solution or are computationally inefficient, because models involve too many 0-1 variables and because use of just a single computer. This study proposes a distributed computation method for solving a packing problem by a set of personal computers via the Internet. First, the traditional packing optimization model is converted into an equivalent model containing many fewer 0-1 variables. Then the model is decomposed into several sub-problems by dividing the objective value into many intervals. Each of these sub-problems is a linearized logarithmic program expressed as a linear mixed 0-1 problem. The whole problem is solvable and reaches a globally optimal solution. The numerical examples demonstrate that the proposed method can obtain the global optimum of a packing problem effectively.
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    Using rough set and worst practice DEA in business failure prediction
    (2005)
    Shuai, JJ
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    Li, HL
    ;
    資訊管理與財務金融系 註：原資管所+財金所
    ;
    Department of Information Management and Finance
    This paper proposes a hybrid approach that predicts the failure of firms based on the past business data, combining rough set approach and worst practice data envelopment analysis (DEA). The worst practice DEA can identify worst performers (in quantitative financial data) by placing them on the frontier while the rules developed by rough set uses non-financial information to predict the characteristics of failed firms. Both DEA and rough set are commonly used in practice. Both have limitations. The hybrid model Rough DEA takes the best of both models, by avoiding the pitfalls of each. For the experiment, the financial data of 396 Taiwan firms during the period 2002-2003 were selected. The results show that the hybrid approach is a promising alternative to the conventional methods for failure prediction.
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    Answering queries on graphical decision models with fuzzy parameters: Algorithms and comparisons
    (2004)
    Kao, HY
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    Huang, CH
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    Li, HL
    ;
    資訊管理與財務金融系 註：原資管所+財金所
    ;
    Department of Information Management and Finance
    The reasoning strategy is a critical component for expert systems or decision support systems. After thorough self-evaluation and environmental scan, the knowledge workers need a reliable mechanism for diagnosis and an optimal suggestion for problem solving. This paper designs a reasoning model with a well-known graphical decision model, influence diagrams. In the proposed model, fuzzy probabilities as well as crisp probabilities are involved. The influence diagrams work compactly in problem diagnosis and decision making aid. Two algorithms to handle the fuzzy parameters, piecewise linearization and alpha-cut methods will be implemented and compared.
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    Interval piecewise regression model with automatic change-point detection by quadratic programming
    (2005-06-01)
    Yu, JR
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    Tzeng, GH
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    Li, HL
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    科技管理研究所
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    Institute of Management of Technology
    To handle large variation data, an interval piecewise regression method with automatic change-point detection by quadratic programming is proposed as an alternative to Tanaka and Lee's method. Their unified quadratic programming approach can alleviate the phenomenon where some coefficients tend to become crisp in possibilistic regression by linear programming and also obtain the possibility and necessity models at one time. However, that method can not guarantee the existence of a necessity model if a proper regression model is not assumed especially with large variations in data. Using automatic change-point detection, the proposed method guarantees obtaining the necessity model with better measure of fitness by considering variability in data. Without piecewise terms in estimated model, the proposed method is the same as Tanaka and Lee's model. Therefore, the proposed method is an alternative method to handle data with the large variations, which not only reduces the number of crisp coefficients of the possibility model in linear programming, but also simultaneously obtains the fuzzy regression models, including possibility and necessity models with better fitness. Two examples are presented to demonstrate the proposed method.