Multicriteria decision-making combining fuzzy set theory, ideal and anti-ideal points for location site selection

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.