single-jc.php

JACIII Vol.5 No.3 pp. 128-138
doi: 10.20965/jaciii.2001.p0128
(2001)

Paper:

Application of Fuzzy Set Theory and DEA Model to Evaluating Production Efficiency for Taipei City Bus Company

Gwo-Hshiung Tzeng, Cheng-Min Feng and Chao-Chung Kang

Energy and Environmental Research Group, Institute of Technology Management and Institute of Traffic and Transportation, College of Management, National Chiao Tung University, 1001, Ta-Hsuch Rd., Hsinchu 300, Taiwan

Received:
February 15, 2001
Accepted:
March 15, 2001
Published:
May 20, 2001
Keywords:
DEA, efficiency, fuzzy DEA, DEA forecast model, fuzzy regression model
Abstract

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.

Cite this article as:
Gwo-Hshiung Tzeng, Cheng-Min Feng, and Chao-Chung Kang, “Application of Fuzzy Set Theory and DEA Model to Evaluating Production Efficiency for Taipei City Bus Company,” J. Adv. Comput. Intell. Intell. Inform., Vol.5, No.3, pp. 128-138, 2001.
Data files:

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 30, 2021