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F. Hosseinzadeh Lotfi

Researcher at Islamic Azad University

Publications -  210
Citations -  4131

F. Hosseinzadeh Lotfi is an academic researcher from Islamic Azad University. The author has contributed to research in topics: Data envelopment analysis & Computer science. The author has an hindex of 27, co-authored 180 publications receiving 3568 citations. Previous affiliations of F. Hosseinzadeh Lotfi include Islamic Azad University, Science and Research Branch, Tehran.

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Extension of the TOPSIS method for decision-making problems with fuzzy data

TL;DR: The aim of this paper is to extend the TOPSIS method to decision-making problems with fuzzy data, and the rating of each alternative and the weight of each criterion are expressed in triangular fuzzy numbers.
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An algorithmic method to extend TOPSIS for decision-making problems with interval data

TL;DR: By extension of TOPSIS method, an algorithm to determine the most preferable choice among all possible choices, when data is interval, is presented.
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Solving a full fuzzy linear programming using lexicography method and fuzzy approximate solution

TL;DR: The concept of the symmetric triangular fuzzy number is used and an approach to defuzzify a general fuzzy quantity is introduced and the FFLP transform to multi objective linear programming (MOLP) where all variables and parameters are crisp.
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Extension of TOPSIS for decision-making problems with interval data: Interval efficiency

TL;DR: This research presents a new TOPSIS method for ranking DMUs with interval data yielding the interval score for each alternative, and it is shown that when data is deterministic, the new method is the same as the conventional one.
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A note on some of dea models and finding efficiency and complete ranking using common set of weights

TL;DR: This paper presents an alternative proof that, if one component of output or input vectors of a DMU dominates the corresponding component of other DMUs whatever the value of other components of this DMU may be, then that DMU is efficient in some of the DEA models.