On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
An efficient approach to solving fuzzy MADM problems
Fuzzy Sets and Systems
On the issue of obtaining OWA operator weights
Fuzzy Sets and Systems
An integrated fuzzy multi-criteria decision making method for manufacturing strategies
Computers and Industrial Engineering
Extensions of the TOPSIS for group decision-making under fuzzy environment
Fuzzy Sets and Systems
A fuzzy approach to select the location of the distribution center
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
A method for fuzzy risk analysis based on the new similarity of trapezoidal fuzzy numbers
Expert Systems with Applications: An International Journal
A method for group decision-making based on multi-granularity uncertain linguistic information
Expert Systems with Applications: An International Journal
Using fuzzy MCDM to select partners of strategic alliances for liner shipping
Information Sciences: an International Journal
Mathematical and Computer Modelling: An International Journal
Group decision-making based on concepts of ideal and anti-ideal points in a fuzzy environment
Mathematical and Computer Modelling: An International Journal
Combining grey relation and TOPSIS concepts for selecting an expatriate host country
Mathematical and Computer Modelling: An International Journal
A fuzzy TOPSIS model via chi-square test for information source selection
Knowledge-Based Systems
Environmental Modelling & Software
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Decision-making problems often involve a complex decision-making process in which multiple requirements and uncertain conditions have to be taken into consideration simultaneously. We are often required to deal with uncertainty, subjectiveness and imprecise data, which are represented by fuzzy data. In this paper, we consider the ideal solution and the anti-ideal solution and assess each alternative in terms of distance as well as similarity to the ideal solution and the anti-ideal solution. To minimize the error, the normalization of fuzzy data is carefully avoided. To get greater accuracy in ranking fuzzy rating, we use the latest and advanced similarity measure. Distance and similarity measures for fuzzy numbers are used and aggregation is guided by the decision rules in order to construct decision function. Further, OWA operators with maximal entropy are used to aggregate across all criteria and the overall score of each alternative is determined The proposed method is more flexible in modeling the decision maker's preferences and more appropriate and effective to handle multicriteria problems of considerable complexity.