scholarly journals A classification of DEA models when the internal structure of the Decision Making Units is considered

2008 ◽  
Vol 173 (1) ◽  
pp. 207-235 ◽  
Author(s):  
Lorenzo Castelli ◽  
Raffaele Pesenti ◽  
Walter Ukovich
Keyword(s):  
Decision Making ◽  
Internal Structure ◽  
Dea Models ◽  
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.

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.

scholarly journals Efficiency evaluation of a small number of DMUs: an approach based on Li and Reeves's model

2009 ◽  
Vol 29 (1) ◽  
pp. 97-110 ◽  
Author(s):  
João Carlos Correia Baptista Soares de Mello ◽  
João Carlos Namorado Clímaco ◽  
Lidia Angulo Meza
Keyword(s):  
Decision Making ◽  
Numerical Data ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Objective Functions ◽  
Dea Models ◽  
Evaluation Measure ◽  
Decision Making Units ◽  
Joint Evaluation ◽  
Do So

This paper deals with the evaluation of Decision Making Units (DMU) when their number is not large enough to allow the use of classic Data Envelopment Analysis (DEA) models. To do so, we take advantage of the TRIMAP software when used to study the Li and Reeves MultiCriteria DEA (MCDEA) model. We introduce an evaluation measure obtained with the integration of one of the objective functions along the weight space. This measure allows the DMUs joint evaluation. This approach is exemplified with numerical data from some Brazilian electrical companies.

Fuzzy integer-valued data envelopment analysis

2018 ◽  
Vol 52 (4-5) ◽  
pp. 1429-1444 ◽  
Author(s):  
Sohrab Kordrostami ◽  
Alireza Amirteimoori ◽  
Monireh Jahani Sayyad Noveiri
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Real World ◽  
Data Envelopment ◽  
First Case ◽  
Dea Models ◽  
Analysis Models ◽  
Fuzzy Dea ◽  
Decision Making Units ◽  
Provided Examples

In conventional data envelopment analysis (DEA) models, the efficiency of decision making units (DMUs) is evaluated while data are precise and continuous. Nevertheless, there are occasions in the real world that the performance of DMUs must be calculated in the presence of vague and integer-valued measures. Therefore, the current paper proposes fuzzy integer-valued data envelopment analysis (FIDEA) models to determine the efficiency of DMUs when fuzzy and integer-valued inputs and/or outputs might exist. To illustrate, fuzzy number ranking and graded mean integration representation methods are used to solve some integer-valued data envelopment analysis models in the presence of fuzzy inputs and outputs. Two examples are utilized to illustrate and clarify the proposed approaches. In the provided examples, two cases are discussed. In the first case, all data are as fuzzy and integer-valued measures while in the second case a subset of data is fuzzy and integer-valued. The results of the proposed models indicate that the efficiency scores are calculated correctly and the projections of fuzzy and integer factors are determined as integer values, while this issue has not been discussed in fuzzy DEA, and projections may be estimated as real-valued data.

scholarly journals An Extension of Cross Redundancy of Interval Scale Outputs and Inputs in DEA

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Farhad Hosseinzadeh-Lotfi ◽  
Gholam-Reza Jahanshahloo ◽  
Mansour Mohammadpour
Keyword(s):  
Decision Making ◽  
Linear Combination ◽  
Data Envelopment ◽  
Ratio Scale ◽  
Interval Scale ◽  
Output Variable ◽  
Input And Output ◽  
Dea Models ◽  
Decision Making Units ◽  
Selection Of

It is well known that data envelopment analysis (DEA) models are sensitive to selection of input and output variables. As the number of variables increases, the ability to discriminate between the decision making units (DMUs) decreases. Thus, to preserve the discriminatory power of a DEA model, the number of inputs and outputs should be kept at a reasonable level. There are many cases in which an interval scale output in the sample is derived from the subtraction of nonnegative linear combination of ratio scale outputs and nonnegative linear combination of ratio scale inputs. There are also cases in which an interval scale input is derived from the subtraction of nonnegative linear combination of ratio scale inputs and nonnegative linear combination of ratio scale outputs. Lee and Choi (2010) called such interval scale output and input a cross redundancy. They proved that the addition or deletion of a cross-redundant output variable does not affect the efficiency estimates yielded by the CCR or BCC models. In this paper, we present an extension of cross redundancy of interval scale outputs and inputs in DEA models. We prove that the addition or deletion of a cross-redundant output and input variable does not affect the efficiency estimates yielded by the CCR or BCC models.

RANKING DECISION ALTERNATIVES BY INTEGRATED DEA, AHP AND GOWER PLOT TECHNIQUES

2008 ◽  
Vol 07 (02) ◽  
pp. 241-258 ◽  
Author(s):  
HAN-LIN LI ◽  
LI-CHING MA
Keyword(s):  
Decision Making ◽  
Iterative Method ◽  
Analytic Hierarchy Process ◽  
Decision Makers ◽  
Analytic Hierarchy ◽  
Dea Models ◽  
Decision Alternatives ◽  
Hierarchy Process ◽  
Decision Making Units ◽  
Data Envelop Analysis

Data Envelop Analysis (DEA) and Analytic Hierarchy Process (AHP) are widely used methods in ranking decision alternatives. However, current DEA models are difficult to discriminate decision-making units through articulating the decision makers' preferences. While AHP and Gower plot models have to specify complete pairwise preferences without providing assisting information. This study develops an iterative method of ranking decision alternatives by integrating DEA, AHP and Gower plot techniques. The developed method first utilizes a modified DEA model to narrow the ranges of a decision maker's preferences. Then, the tentative ranks of the decision alternatives, computed by embedding the decision maker's preferences, are depicted via Gower plots to illustrate the cardinal and ordinal inconsistencies of these preferences. The decision maker then adjusts the preferences iteratively until the inconsistencies are within the tolerance.

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