Consistent Construction of Evaluation Threshold Values and Rules for Heterogeneous Linguistic Input Information

Abstract

This study firstly proposes a simpler method for evaluating one certain object’s quality with multiple criteria according to some preset evaluation threshold values that are real numbers. In real life, numerous individual valuations are provided with distributional linguistic input information and with multiple criteria, and thus they can become heterogeneous. Against this background, by using OWA weight functions we propose an extended setting and some methods to generate distributional evaluation threshold values which are suitable for the corresponding thresholds-based evaluation method. Some special definitions and formulations are also well provided with necessary analyses and comments. A numerical example of reservoir evaluation and effect are also illustrated.

Unsymmetrical basic uncertain information with some decision-making methods

“…We can reasonably obtain an UBUI by considering the known value x and the statistics (a probability vector (y L , y U , y, z)) with a transformation function S : [0, 1] × W (4) → UB: S(x, (y L , y U , y, z)) = (x, (y L + y, y U + y)) (1) With any obtained UBUI granule (x, (c L , c U )), we use the following formula T * : UB → I to transform it into an interval which sometime can be much easier to handle in decision making.…”

“…Still consider the above illustrated example of university teacher performance evaluation. Assume μ : 2 [4] → [0, 1] represents the interrelated importance between the four criteria: (i) academic performance; (ii) teaching spirit and attitude; (iii) teaching effect; (iv) social service done. μ satisfies μ(∅) = μ({2}) = μ({3}) = μ({4}) = 0, and μ(A) = 1 whenever A / ∈ {∅, {2}, {3}, {4}}.…”

“…Uncertainty is pervasive in a wide range of decision making and evaluation problems, from involved information type [1][2][3][4][5][6] to decision methodology and result [7,8]. Some typical quantitative uncertain information includes interval information, probability information, fuzzy information [9], and their related extension or generalization forms [10][11][12][13].…”

Two-Sided Matching Decision-Making in an Incomplete and Heterogeneous Context: A Optimization-Based Method

“…In practice, the most common way for expressing the fuzzy stable demand is to use the linguistic information. And the use of the linguistic information usually implies the demand of the techniques of computing with words (CW) [28][29][30][31][32][33]. In this paper, we will use the 2-tuple fuzzy linguistic representations [28] to deal with the linguistic information.…”