A new fuzzy additive model for determining the common set of weights in Data Envelopment Analysis

Adel Azar, Mohammad Zarei Mahmoudabadi, Ali Emrouznejad*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The main advantage of Data Envelopment Analysis (DEA) is that it does not require any priori weights for inputs and outputs and allows individual DMUs to evaluate their efficiencies with the input and output weights that are only most favorable weights for calculating their efficiency. It can be argued that if DMUs are experiencing similar circumstances, then the pricing of inputs and outputs should apply uniformly across all DMUs. That is using of different weights for DMUs makes their efficiencies unable to be compared and not possible to rank them on the same basis. This is a significant drawback of DEA; however literature observed many solutions including the use of common set of weights (CSW). Besides, the conventional DEA methods require accurate measurement of both the inputs and outputs; however, crisp input and output data may not relevant be available in real world applications. This paper develops a new model for the calculation of CSW in fuzzy environments using fuzzy DEA. Further, a numerical example is used to show the validity and efficacy of the proposed model and to compare the results with previous models available in the literature.

Original languageEnglish
Pages (from-to)61-69
Number of pages9
JournalJournal of Intelligent and Fuzzy Systems
Issue number1
Publication statusPublished - 17 Aug 2015


  • common set of weights
  • Data Envelopment Analysis
  • fuzzy DEA


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