An analytical solution to fuzzy TOPSIS and its application in personnel selection for knowledge-intensive enterprise

• Based on Karnik-Mendel (KM) algorithm, we propose an analytical solution to fuzzy TOPSIS method, and apply it in personnel selection problems for knowledge-intensive enterprise.
• Compared with the existing TOPSIS method, it obtains accurate fuzzy relative closeness instead of the crisp point or approximate fuzzy relative closeness estimate.
• The global picture of fuzzy relative closeness provides a way to further discuss the inner properties of fuzzy TOPSIS method.

Personnel selection is a critical enterprise strategic problem in knowledge-intensive enterprise. Fuzzy number which can be described as triangular (trapezoid) fuzzy number is an adequate way to assess the evaluation and weights for the alternatives. In that case, fuzzy TOPSIS, as a classic fuzzy multiple criteria decision making (MCDM) methods, has been applied in personnel selection problems. Currently, all the researches on this topic either apply crisp relative closeness but causing information loss, or employ fuzzy relative closeness estimate but with complicated computation to rank the alternatives. In this paper, based on Karnik–Mendel (KM) algorithm, we propose an analytical solution to fuzzy TOPSIS method. Some properties are discussed, and the computation procedure for the proposed analytical solution is given as well. Compared with the existing TOPSIS method for personnel selection problem, it obtains accurate fuzzy relative closeness instead of the crisp point or approximate fuzzy relative closeness estimate. It can both avoid information loss and keep computational efficiency in some extent. Moreover, the global picture of fuzzy relative closeness provides a way to further discuss the inner properties of fuzzy TOPSIS method. Detailed comparisons with approximate fuzzy relative closeness method are provided in personnel selection application.

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