Ranking Decision-Making Units Using Double-Frontier Analysis Approach
Authors
Abstract:
Data envelopment analysis is a nonparametric method for measuring the performance of a set of decision-making units (DMUs) that consume multiple inputs to produce multiple outputs. Using this approach, the performance of DMUs is measured from both optimistic and pessimistic views. However, their results are very misleading and even contradictory in many cases. Indisputably, different performance measures should be combined for an overall assessment of the performance of each DMU. This is known as double-frontier analysis. This article proposes a power-averaged efficiency measure to evaluate the overall performance of each DMU. The power-averaged efficiency combines both optimistic and pessimistic efficiencies of each DMU, and therefore, is more comprehensive than both measures. The results showed the higher differentiation capability of the power-averaged efficiency than both optimistic and pessimistic efficiency measures. The efficiency of the proposed power-averaged efficiency was demonstrated through a numerical example on evaluation of the performance of 42 departments in one of the Islamic Azad University branches to reveal its capabilities in real life situations.
similar resources
Ranking Decision Making Units, using Non-radial Model, applying Bootstrap
Data envelopment analysis (DEA) is a mathematical programming method in Operations Research that can be used to distinguish between efficient and inefficient decision making units (DMUs). However, the conventional DEA models do not have the ability to rank the efficient DMUs. This article suggests bootstrapping method for ranking measures of technical efficiency as calculated via non-radial mod...
full textranking decision making units, using non-radial model, applying bootstrap
data envelopment analysis (dea) is a mathematical programming method in operations research that can be used to distinguish between efficient and inefficient decision making units (dmus). however, the conventional dea models do not have the ability to rank the efficient dmus. this article suggests bootstrapping method for ranking measures of technical efficiency as calculated via non-radial mod...
full textRanking Decision Making Units in Fuzzy-DEA Using Entropy
Abstract Data Envelopment Analysis (DEA) can be regarded as a useful management tool to the assessment evaluation of decision making units (DMUs) using multiple inputs to produce multiple outputs. In some cases, to evaluate the efficiency having imprecise inputs and outputs such as fuzzy or interval data the efficiency of DMUs won’t be exact as well. Most researches have been conducted were bas...
full textRanking Efficient Decision Making Units in Data Envelopment Analysis based on Changing Reference Set
One of the drawbacks of Data Envelopment Analysis (DEA) is the problem of lack of discrimination among efficient Decision Making Units (DMUs). A method for removing this difficulty is called changing reference set proposed by Jahanshahloo and et.al (2007). The method has some drawbacks. In this paper a modified method and new method to overcome this problems are suggested. The main advantage of...
full textBenchmark Forecasting in Data Envelopment Analysis for Decision Making Units
Although DEA is a powerful method in evaluating DMUs, it does have some limitations. One of the limitations of this method is the result of the evaluation is based on previously data and the results are not proper for forecasting the future changes. So For this purpose, we design feedback loops for forecasting inputs and outputs through system dynamics and simulation. Then we use DEA model to f...
full textThe Comparison of Principal Component Analysis and Data Envelopment Analysis in Ranking of Decision Making Units
In this study, Data Envelopment Analysis (DEA) and Principal Component Analysis (PCA) were compared when these two methods are used for ranking Decision Making Units (DMU) with multiple inputs and outputs. DEA, a nonstatistical technique, is a methodology using a linear programming model for evaluating and ranking DMU’s performance. PCA, a multivariate statistical method, uses new measures defi...
full textMy Resources
Journal title
volume 17 issue 1
pages 103- 118
publication date 2020-03
By following a journal you will be notified via email when a new issue of this journal is published.
No Keywords
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023