Reasonable properties for the ordering of fuzzy quantities (II)
Fuzzy Sets and Systems
A preference aggregation method through the estimation of utility intervals
Computers and Operations Research
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
An extended TOPSIS for determining weights of decision makers with interval numbers
Knowledge-Based Systems
Interval-valued fuzzy TOPSIS method with leniency reduction and an experimental analysis
Applied Soft Computing
Extension of TOPSIS for decision-making problems with interval data: Interval efficiency
Mathematical and Computer Modelling: An International Journal
On rank reversal in decision analysis
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Hi-index | 12.05 |
The TOPSIS method is a technique for order preference by similarity to ideal solution. This technique currently is one of the most popular methods for Multiple Criteria Decision Making (MCDM). The TOPSIS method was primary developed for dealing with only real-valued data. In many cases, it is hard to present precisely the exact ratings of alternatives with respect to local criteria and as a result these ratings are considered as intervals. There are some papers devoted to the interval extensions of TOPSIS method, but these extensions are based on different heuristic approaches to definition of positive and negative ideal solutions. These ideal solutions are presented by real values or intervals, which are not attainable in a decision matrix. Since this is in contradiction with basics of classical TOPSIS method, in this paper we propose a new direct approach to interval extension of TOPSIS method which is free of heuristic assumptions and limitations of known methods. Using numerical examples we show that ''direct interval extension of TOPSIS method'' may provide the final ranking of alternatives which is substantially different from the results obtained using known methods.