scholarly journals Finding strong defining hyperplanes of production possibility set with fuzzy data

2016 ◽  
Vol 2016 (1) ◽  
pp. 15-22
Author(s):  
Mehdi Amiri ◽  
Mohsen Rostami Malkhalifeh
Author(s):  
Nazila Aghayi ◽  
Samira Salehpour

Revenue efficiency measurement is one of the most important issues in data envelopment analysis (DEA). Most of the proposed models calculate the revenue efficiency of decision making units (DMUs) which play a main role in the formation of the production possibility set by implementing exact or fuzzy data. The revenue efficiency value of a sample decision making unit with exact and fuzzy data has not been investigated by these models yet. There exist different types of fuzzy numbers, however, only a special type of them has been used in revenue efficiency models with fuzzy data. The concept of vector has not been employed to calculate the measure of the revenue efficiency in any of the studies conducted thus far. However, in this article the authors propose a model for evaluating the revenue efficiency measure of a fuzzy sample DMU without the limitations of previous models with regards to the formation of the production possibility set. In the proposed model, data can be selected from different types of fuzzy numbers and there is no limitation on the type of the used fuzzy data. In addition, the current article employs the concept of vector for revenue efficiency assessment for the first time.


2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
A. Barzegarinegad ◽  
G. Jahanshahloo ◽  
M. Rostamy-Malkhalifeh

We propose a procedure for ranking decision making units in data envelopment analysis, based on ideal and anti-ideal points in the production possibility set. Moreover, a model has been introduced to compute the performance of a decision making unit for these two points through using common set of weights. One of the best privileges of this method is that we can make ranking for all decision making units by solving only three programs, and also solving these programs is not related to numbers of decision making units. One of the other advantages of this procedure is to rank all the extreme and nonextreme efficient decision making units. In other words, the suggested ranking method tends to seek a set of common weights for all units to make them fully ranked. Finally, it was applied for different sets holding real data, and then it can be compared with other procedures.


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