A fuzzy multiple objective programming to DEA with imprecise data

Authors:
Jing-Rung Yu;Yen-Chen Tzeng;Gwo-Hshiung Tzeng;Tzu-Yi Yu;Her-Jiun Sheu
Affiliations:
Department of Information Management, National Chi Nan University, Puli Nantou 545, Taiwan;Inotera Memories Inc. Hwa-Ya Technology Park, 667, Fu Hsing 3rd Rd, Kueishan, Taoyuan, Taiwan;Institute of Technology Management, National Chiao Tung University, Hsinchu 30050, Taiwan;Department of Information Management, National Chi Nan University, Puli Nantou 545, Taiwan;Department of Management Science, National Chiao Tung University, Hsinchu 30050, Taiwan
Venue:
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Year:
2005

Citing 3
Cited 4

A note on including ordinal relations among multipliers in data envelopment analysis

Management Science
Fuzzy multiple objective programming and compromise programming with Pareto optimum

Fuzzy Sets and Systems
Idea and Ar-Idea: Models for Dealing with Imprecise Data in Dea

Management Science

Recent Literature Collected by Didier DUBOIS, Henri PRADE and Salvatore SESSA

Fuzzy Sets and Systems
Fuzzy multiattribute grey related analysis using DEA

Computers & Mathematics with Applications
Fuzzy dynamic multi-objective Data Envelopment Analysis model

Expert Systems with Applications: An International Journal
Evaluating top information technology firms in standard and poor's 500 index by using a multiple objective programming based data envelopment analysis

IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

A fuzzy multiple objective programming approach to data envelopment analysis (DEA) of imprecise data is proposed in this paper. The problems involving a mixture of imprecise and exact data for all decision making units (DMUs) could be resolved and the discriminating power of imprecise DEA (IDEA) is enhanced. Although Cooper et al. have developed IDEA to overcome the issues of imprecise data, the discriminating power is not satisfactory since too many efficient DMUs are derived. Chiang and Tzeng's approach using fuzzy multiple objective programming techniques is adopted to enhance the discriminating power of IDEA. The same data set of Cooper et al. is employed to illustrate the merit of our approach.