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Application of Fuzzy Set Theory and DEA Model to Evaluating Production Efficiency for Taipei City Bus Company
Author(s) -
GwoHshiung Tzeng,
ChengMin Feng,
Chao-Chung Kang
Publication year - 2001
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 20
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2001.p0128
Subject(s) - computer science , fuzzy logic , fuzzy set , production (economics) , set (abstract data type) , operations research , mathematical optimization , data mining , artificial intelligence , mathematics , economics , macroeconomics , programming language
The purpose of this paper is to present a performance evaluation model for forecasting production efficiency for Decision Making Units (DMUs). This model is based on the fuzzy set theory, fuzzy regression, and the DEA model. A stochastic DEA approach has been proposed and used widely to analyze the performance of the uncertain input or output data, but this approach requires large data samples and assumes probability distribution in measurement error terms. The concept of fuzzy numbers was seldom considered, although the stochastic DEA approach can be used for prediction. This paper integrates fuzzy regression and fuzzy DEA as one model. The results of this research show that the model developed in this paper is applicable to evaluate the "reform policy for passenger loading operations" currently undertaken by the Taipei City Bus Company. Based on this study, the integration of fuzzy numbers, fuzzy regression, and the DEA model can be applied to evaluate production efficiency of the city bus company for the short-term future.

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