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Showing 2 results for Common Set of Weights
Finding a Common Set of Weights by the Fuzzy Entropy Compared with Data Envelopment Analysis - A Case Study
R. Tavakkoli-Moghaddam, S. Mahmoodi,
Volume 21, Issue 2 (5-2010)
Abstract

  A data envelopment analysis (DEA) method can be regarded as a useful management tool to evaluate decision making units (DMUs) using multiple inputs and outputs. In some cases, we face with imprecise inputs and outputs, such as fuzzy or interval data, so the efficiency of DMUs will not be exact. Most researchers have been interested in getting efficiency and ranking DMUs recently. Models of the traditional DEA cannot provide a completely ranking of efficient units however, it can just distinguish between efficient and inefficient units. In this paper, the efficiency scores of DMUs are computed by a fuzzy CCR model and the fuzzy entropy of DMUs. Then these units are ranked and compared with two foregoing procedures. To do this, the fuzzy entropy based on common set of weights (CSW) is used. Furthermore, the fuzzy efficiency of DMUs considering the optimistic level is computed. Finally, a numerical example taken from a real-case study is considered and the related concept is analyzed.


DEA common set of weights based on a multi objective Fractional Programming
Seyed Hossein Razavi Hajiagha, Shide Sadat Hashemi, Hannan Amoozad Mahdiraji,
Volume 25, Issue 3 (7-2014)
Abstract
Data envelopment analysis operates as a tool for appraising the relative efficiency of a set of homogenous decision making units. This methodology is applied widely in different contexts. Regarding to its logic, DEA allows each DMU to take its optimal weight in comparison with other DMUs while a similar condition is considered for other units. This feature is a bilabial characteristic which optimizes the performance of units in one hand. This flexibility on the other hand threats the comparability of different units because different weighting schemes are used for different DMUs. This paper proposes a unified model for determination of a common set of weights to calculate DMUs efficiency. This model is developed based on a multi objective fractional linear programming model that considers the original DEA's results as ideal solution and seeks a set of common weights that rank the DMUs and increase the model's discrimination power. Comparison of the proposed method with some of the previously presented models has shown its advantages as a DMUs ranking model.
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