TOPSIS-RTCID for range target-based criteria and interval data

Target setting in additive models with preferences and interval data

Multi-criteria Decision Making Models by Applying the Topsis Method to Crisp and Interval Data

In this paper, one of the multi-criteria models in making decision, a Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), is described. Some of the advantages of TOPSIS methods are: simplicity, rationality, comprehensibility, good computational efficiency and ability to measure the relative performance for each alternative in a simple mathematical form. The paper has a r...

Ranking Bank Branches with Interval Data By IAHP and TOPSIS

This paper proposes a method for ranking decision making units (DMUs) using some of the multiple criteria decision making / multiple attribute decision making          (MCDM /MADM) techniques, namely, interval analytic hierarchy process (IAHP)          and the technique for order preference by similarity to an ideal solution (TOPSIS).          Since the efficiency score of unity is assigned to ...

Ranking Bank Branches with Interval Data By IAHP and TOPSIS

This paper proposes a method for ranking decision making units (DMUs) using some of the multiple criteria decision making / multiple attribute decision making          (MCDM /MADM) techniques, namely, interval analytic hierarchy process (IAHP)          and the technique for order preference by similarity to an ideal solution (TOPSIS).          Since the efficiency score of unity is assigned to ...

A New Entropy Weight for Sub-Criteria in Interval Type-2 Fuzzy TOPSIS and Its Application

Fuzzy Technique for Order Preference by Similarly to Ideal Solution (TOPSIS) is one of the most commonly used approaches in solving numerous multiple criteria decision making problems. It has been widely used in ranking of multiple alternatives with respect to multiple criteria with the superiority of fuzzy set type-1 and subjective weights. Recently, fuzzy TOPSIS has been merged with interval ...