Evaluation of Economic Efficiency of Industrial Enterprises Using Connection Number and Stochastic Simulation of Triangular Fuzzy Numbers | IEEE Conference Publication | IEEE Xplore

Evaluation of Economic Efficiency of Industrial Enterprises Using Connection Number and Stochastic Simulation of Triangular Fuzzy Numbers


Abstract:

The evaluation of economic efficiency of industrial enterprises is a comprehensive uncertainty system problem. Based on the theory of set pair analysis, stochastic simula...Show More

Abstract:

The evaluation of economic efficiency of industrial enterprises is a comprehensive uncertainty system problem. Based on the theory of set pair analysis, stochastic simulation of triangular fuzzy numbers, a novel evaluation method for the economic efficiency of industrial enterprises was investigated in this paper. A concept of multi-element connection number of the set pair, which was consisted of evaluation index sets and classification standard sets of the economic efficiency of industrial enterprises, was introduced to express the hierarchy and fuzziness of membership between the evaluation sample and classification standard. Moreover, the triangular fuzzy number simulated by Monte-Carlo method was presented to depict the changing process and fuzziness of component coefficients of discrepancy degree. Combined with the weight of evaluation index, the integrated connection number was calculated to assess the grade of economic efficiency of industrial enterprises of the sample. The evaluation in form of a confidence interval corresponding to a confidence level was also given. Finally, a practical example was described to confirm and to compare with the extension method. The results show that the proposed model is more feasible and easy to operate, and the result is good. It can make full use of the uncertainty information of evaluation index and grade classification standard for the economic efficiency of industrial enterprises, and resolve the difficulties of traditional operations of triangular fuzzy numbers and their functions.
Date of Conference: 07-09 May 2010
Date Added to IEEE Xplore: 30 September 2010
ISBN Information:
Conference Location: Guangzhou, China

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