Ranking data with BASIC

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 ...

Ranking Ecient DMUs with Stochastic Data by Considering Inecient Frontier

A new attitude coupled with the basic thinking to ordering for ranking fuzzy ‎numbers

‎Ranking fuzzy numbers is generalization of the concepts of order, and class, and so have fundamental applications. Moreover, deriving the final efficiency and powerful ranking are helpful to decision makers when solving fuzzy problems. Selecting a good ranking method can apply to choosing a desired criterion in a fuzzy environment. There are numerous methods proposed for the ranking of fuzzy n...

Heuristic for efficiency ranking with fuzzy data

In this paper, we have developed a heuristic that uses Data Envelopment Analysis (DEA) to rank Decision Making Units (DMUs) with fuzzy input and output values in the order of their probable efficiency. The heuristic is designed to be used with the raw samples of the fuzzy data, and does not require knowledge of the fuzzy data’s probability distribution. It is meant to be a simple method of diff...