Abstract
Evaluation based on Distance from Average Solution (EDAS) is a new multicriteria decision making (MCDM) method, which is based on the distances of alternatives from the average scores of attributes. Classical EDAS has been already extended by using ordinary fuzzy sets in case of vague and incomplete data. In this paper, we propose an interval-valued intuitionistic fuzzy EDAS method, which is based on the data belonging to membership, nonmembership, and hesitance degrees. A sensitivity analysis is also given to show how robust decisions are obtained through the proposed intuitionistic fuzzy EDAS. The proposed intuitionistic fuzzy EDAS method is applied to the evaluation of solid waste disposal site selection alternatives. The comparative and sensitivity analyses are also included.
Highlights
Evaluation based onSuDbismtaintcteedfr6omMAavr.er2a0ge12S;oaluctcioenpt(eEdD1A4S)Aius ga. n2e0w12multicriteria decision making (MCDM) method, which is based on the distances of alternatives from the average scores of attributes
The evaluation of alternatives in this method is based on distances of each alternative from the average solution with respect to each criterion
We have proposed the interval-valued intuitionistic fuzzy Evaluation Based on Distance from Average Solution (EDAS) (IVIF EDAS) method in this paper
Summary
Curiosity of researchers to inventing new MCDM methods is getting competitive. Trends of MCDM methods development since initial days showed enormous interest among researchers in this area to build robust structures in order to handle complex decisions. In EDAS method, first two measures are delivered as the positive distance from average (PDA), and the negative distance from average (NDA) These measures can show the difference between each solution (alternative) and the average solution. The positive distance from average (PDA) and the negative distance from average (NDA) matrixes need to be calculated in this step according to lower and upper values of matrix as shown: PDAij max(0,(xij − AVj. The positive distance from average (PDA) and the negative distance from average (NDA) matrixes need to be calculated in this step according to lower and upper values of matrix as shown: PDAij max(0,(xij − AVj In this way PDAij and NDAij represent the positive and negative distance of ith alternative from average solution in terms of jth criterion for the lower level of decision matrix, respectively. The alternative with the highest ASi is the best choice among the candidate alternatives
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