Browsing by Author "Huang, Jih-Jeng"

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Marketing segmentation using support vector clustering
(2007-02-01)
Huang, Jih-Jeng
;
Tzeng, Gwo-Hshiung
;
ong, Chorng-Shy Ong
;
科技管理研究所
;
Institute of Management of Technology
Marketing segmentation is widely used for targeting a smaller market and is useful for decision makers to reach all customers effectively with one basic marketing mix. Although several clustering algorithms have been proposed to deal with marketing segmentation problems, a soundly method seems to be limited. In this paper, support vector clustering (SVC) is used for marketing segmentation. A case study of a drink company is used to demonstrate the proposed method and compared with the k-means and the self-organizing feature map (SOFM) methods. On the basis of the numerical results, we can conclude that SVC outperforms the other methods in marketing segmentation. (C) 2005 Published by Elsevier Ltd.
NEW THINKING OF MULTI-OBJECTIVE PROGRAMMING WITH CHANGEABLE SPACE - IN SEARCH OF EXCELLENCE
(2014-06-01)
Huang, Jih-Jeng
;
Tzeng, Gwo-Hshiung
;
科技管理研究所
;
Institute of Management of Technology
Multi-objective programming (MOP) is a branch of mathematical programming that has been widely used to deal with various practical problems. With the introduction of new technologies and business models, a paradigm shift in optimization problems is gradually taking place from fixed to flexible optimization. For example, many organizations use outsourcing or business process reengineering (BPR) to improve or upgrade their objective and technological coefficients to achieve better performance. Hence, traditional MOP models should be extended from the concept of fixed to changeable parameters, called changeable space, which includes decision space and objective space. In this paper, we propose three kinds of MOP model with changeable parameters to help decision-makers achieve the desired point (aspiration level), which is better than the ideal point.
Nonlinear Deterministic Frontier Model Using Genetic Programming
(2009)
Chen, Chin-Yi
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Huang, Jih-Jeng
;
Tzeng, Gwo-Hshiung
;
科技管理研究所
;
Institute of Management of Technology
In economics, several parametric regression-based models have been proposed to measure the technical efficiency of decision making units (DMUs). However, the problem of misspecification restricts the use of these methods. In this paper, symbolic regression is employed to obtain the approximate optimal production function automatically using genetic programming (GP). Monte Carlo simulation is used to compare the performance of data envelopment analysis (DEA), deterministic frontier analysis (DFA) and GP-based DFA with respect to three different production functions and sample sizes. The simulated results indicated that the proposed method has better performance than that of others with respect to nonlinear production functions.