This paper presents a similarity-based approach to ranking multicriteria alternatives for solving discrete multicriteria problems. The approach effectively makes use of the ideal solution concept in such a way that the most preferred alternative should have the highest degree of similarity to the positive ideal solution and the lowest degree of similarity to the negative-ideal solution. The overall performance index of each alternative across all criteria is determined based on the concept of the degree of similarity between each alternative and the ideal solution using alternative gradient and magnitude. An example is presented to demonstrate the applicability of the proposed approach. A comparative analysis between the proposed approach and the technique for order preference by similarity to ideal solution is conducted for demonstrating the merits of the proposed approach for solving discrete multicriteria analysis problems. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Deng, H. (2007). A similarity-based approach to ranking multicriteria alternatives. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4682 LNAI, pp. 253–262). Springer Verlag. https://doi.org/10.1007/978-3-540-74205-0_28
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