A data envelopment model for aggregating preference rankings
Management Science
A procedure for ranking efficient units in data envelopment analysis
Management Science
Nearest interval approximation of a fuzzy number
Fuzzy Sets and Systems - Fuzzy intervals
Idea and Ar-Idea: Models for Dealing with Imprecise Data in Dea
Management Science
The appropriate total ranking method using DEA for multiple categorized purposes
Journal of Computational and Applied Mathematics - Special issue: Papers presented at the 1st Sino--Japan optimization meeting, 26-28 October 2000, Hong Kong, China
A preference aggregation method through the estimation of utility intervals
Computers and Operations Research
Selecting the most preferable alternatives in a group decision making problem using DEA
Expert Systems with Applications: An International Journal
Aggregating preference ranking with fuzzy Data Envelopment Analysis
Knowledge-Based Systems
A robust optimization approach for imprecise data envelopment analysis
Computers and Industrial Engineering
A DEA-inspired procedure for the aggregation of preferences
Expert Systems with Applications: An International Journal
Personnel selection using analytic network process and fuzzy data envelopment analysis approaches
Computers and Industrial Engineering
Ranking efficient decision-making units in data envelopment analysis using fuzzy concept
Computers and Industrial Engineering
Computers and Industrial Engineering
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In this paper, a new method for aggregating the opinions of experts in a preferential voting system is proposed. The method, which uses fuzzy concept in handling crisp data, is computationally efficient and is able to completely rank the alternatives. Through this method, the number of votes for certain rank position that each alternative receives are first grouped together to form fuzzy numbers. The nearest point to a fuzzy number concept is then used to introduce an artificial ideal alternative. Data envelopment analysis is next used to find the efficiency scores of the alternatives in a pair-wise comparison with the artificial ideal alternative. Alternatives are rank based on these efficiency scores. If the alternatives are not completely ranked, a weight restriction method also based on fuzzy concept is used on the un-discriminated alternatives until they are completely ranked. Two examples are given for illustration of the method.