Assessing the Relative Efficiency of Decision Making Units Using DEA Models with Weight Restrictions

1998 ◽  
Vol 49 (5) ◽  
pp. 500
Author(s):  
V. V. Podinovski ◽  
A. D. Athanassopoulos
Keyword(s):  
Decision Making ◽  
Relative Efficiency ◽  
Weight Restrictions ◽  
Dea Models ◽  
Decision Making Units

Relative Efficiency of Decision Making Units Producing Both Desirable and Undesirable Outputs: A Case of Textile Processing Units in Pakistan.

2011 ◽  
Vol 50 (4II) ◽  
pp. 685-698
Author(s):  
Samina Khalil
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Value Added ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Textile Processing ◽  
Decision Making Unit ◽  
Desirable And Undesirable Outputs ◽  
Decision Making Units

This paper aims at measuring the relative efficiency of the most polluting industry in terms of water pollution in Pakistan. The textile processing is country‘s leading sub sector in textile manufacturing with regard to value added production, export, employment, and foreign exchange earnings. The data envelopment analysis technique is employed to estimate the relative efficiency of decision making units that uses several inputs to produce desirable and undesirable outputs. The efficiency scores of all manufacturing units exhibit the environmental consciousness of few producers is which may be due to state regulations to control pollution but overall the situation is far from satisfactory. Effective measures and instruments are still needed to check the rising pollution levels in water resources discharged by textile processing industry of the country. JEL classification: L67, Q53 Keywords: Data Envelopment Analysis (DEA), Decision Making Unit (DMU), Relative Efficiency, Undesirable Output

The relative efficiency of decision-making units: a case study of TFT-LCD industry in Taiwan

2007 ◽  
Vol 1 (2) ◽  
pp. 182
Author(s):  
Ming Chun Tsai ◽  
Shu Ping Lin ◽  
Ssu Ying Liu ◽  
Ming (Michael) Chang ◽  
Jason C.H. Chen
Keyword(s):  
Decision Making ◽  
Relative Efficiency ◽  
Decision Making Units

RANKING DECISION MAKING UNITS BY MEANS OF SOFT COMPUTING DEA MODELS

2011 ◽  
Vol 19 (01) ◽  
pp. 115-134 ◽  
Author(s):  
JOSÉ E. BOSCÁ ◽  
VICENTE LIERN ◽  
RAMÓN SALA ◽  
AURELIO MARTÍNEZ
Keyword(s):  
Decision Making ◽  
Statistical Analysis ◽  
Soft Computing ◽  
Ranking Method ◽  
Fuzzy Data ◽  
Trade Off ◽  
Dea Models ◽  
Data Efficiency ◽  
Fuzzy Dea ◽  
Decision Making Units

This paper presents a method for ranking a set of decision making units according to their level of efficiency and which takes into account uncertainty in the data. Efficiency is analysed using fuzzy DEA techniques and the ranking is based on the statistical analysis of cases that include representative situations. The method enables the removal of the sometimes unrealistic hypothesis of a perfect trade-off between increased inputs and outputs. This model is compared with other DEA models that work with imprecise or fuzzy data. As an illustration, we apply our ranking method to the evaluation of a group of Spanish seaports, as well as teams playing in the Spanish football league. We compare the results with other methods and we show that our method enables a total ranking of the seaports, and that the ranking of football teams is found to be more consistent with final league positions.

AGGREGATED RATIO ANALYSIS IN DEA

2005 ◽  
Vol 04 (03) ◽  
pp. 369-384 ◽  
Author(s):  
DESHENG WU ◽  
LIANG LIANG ◽  
ZHIMIN HUANG ◽  
SUSAN X. LI
Keyword(s):  
Decision Making ◽  
Relative Efficiency ◽  
Efficient Frontier ◽  
Ratio Analysis ◽  
Ratio Model ◽  
Analysis Model ◽  
Frontier Analysis ◽  
Dea Model ◽  
Insight Into ◽  
Decision Making Units

This paper proposes an aggregated ratio analysis model which can be utilized to evaluate relative efficiency of decision making units (DMUs). We show that our proposed ratio model is equivalent to the CCR DEA model. This equivalence property offers a great deal of opportunities for DEA to be interpreted and applied in different ways. Our model also offers an insight into the frontier analysis. Whether a DMU is on the frontier or efficient frontier can be informed by using our aggregated ratio analysis. Several results developed in the paper are coincident with that in the literature.

scholarly journals A full investigation of the directional congestion in data envelopment analysis

2019 ◽  
Author(s):  
somayeh khezri ◽  
Akram Dehnokhalaji ◽  
Farhad Hosseinzadeh Lotfi
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Input Output ◽  
Input And Output ◽  
Dea Models ◽  
Systems Of Inequalities ◽  
Full Investigation ◽  
Decision Making Units ◽  
Inputs And Outputs

One of interesting subjects in Data Envelopment Analysis (DEA) is estimation of congestion of Decision Making Units (DMUs). Congestion is evidenced when decreases (increases) in some inputs re- sult in increases (decreases) in some outputs without worsening (im- proving) any other input/output. Most of the existing methods for measuring the congestion of DMUs utilize the traditional de nition of congestion and assume that inputs and outputs change with the same proportion. Therefore, the important question that arises is whether congestion will occur or not if the decision maker (DM) increases or de- creases the inputs dis-proportionally. This means that, the traditional de nition of congestion in DEA may be unable to measure the con- gestion of units with multiple inputs and outputs. This paper focuses on the directional congestion and proposes methods for recognizing the directional congestion using DEA models. To do this, we consider two di erent scenarios: (i) just the input direction is available. (ii) none of the input and output directions are available. For each scenario, we propose a method consists in systems of inequalities or linear pro- gramming problems for estimation of the directional congestion. The validity of the proposed methods are demonstrated utilizing two nu- merical examples.

scholarly journals Designing an Intuitionistic Fuzzy Network Data Envelopment Analysis Model for Efficiency Evaluation of Decision-Making Units with Two-Stage Structures

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Nafiseh Javaherian ◽  
Ali Hamzehee ◽  
Hossein Sayyadi Tooranloo
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
Two Stage ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy ◽  
Dea Models ◽  
Decision Making Units

Data envelopment analysis (DEA) is a powerful tool for evaluating the efficiency of decision-making units for ranking and comparison purposes and to differentiate efficient and inefficient units. Classic DEA models are ill-suited for the problems where decision-making units consist of multiple stages with intermediate products and those where inputs and outputs are imprecise or nondeterministic, which is not uncommon in the real world. This paper presents a new DEA model for evaluating the efficiency of decision-making units with two-stage structures and triangular intuitionistic fuzzy data. The paper first introduces two-stage DEA models, then explains how these models can be modified with intuitionistic fuzzy coefficients, and finally describes how arithmetic operators for intuitionistic fuzzy numbers can be used for a conversion into crisp two-stage structures. In the end, the proposed method is used to solve an illustrative numerical example.

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