Combining grey relational analysis and TOPSIS concepts for evaluating the technical innovation capability of high technology enterprises with fuzzy information
Affiliations: [a] School of Public Affair, Chongqing University, Chongqing, China | [b] School of Management, China University of Mining and Technology, Jiangsu, China
Correspondence:
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Corresponding author. Bang-Jun Wang, School of Management, China University of Mining and Technology, Jiangsu 221116, China. Tel.: +86 13813465302; Fax: +86 13813465302; E-mail: zgkywangbangjun@163.com
Abstract: The aim of this paper is to extend the grey relational analysis (GRA) and Technique for order performance by similarity to ideal solution (TOPSIS) concepts to the fuzzy environment. Owing to vague concepts frequently represented in decision data, the crisp values are inadequate to model real-life situation. In this paper, the rating of each alternative and the weight of each criterion are described by linguistic terms which can be expressed in triangular fuzzy numbers. Then a vertex method is used to calculate the distance between two triangular fuzzy numbers. According to the concept of the GRA and TOPSIS, a fuzzy relative relational degree is defined to determine the ranking order of all alternatives by calculating the degree of fuzzy grey relational coefficient to both the fuzzy positive-ideal solution (FPIS) and fuzzy negative-ideal solution (FNIS) simultaneously. Finally, an example for evaluating the technical innovation capability of high technology enterprises with fuzzy information is given to show the feasibility and effectiveness of the proposed method.
