ORIGINAL_ARTICLE
An Alternative Secondary Goal Approach to Modify Cross Efficiency Evaluation in Data Envelopment Analysis
The cross efficiency evaluation is used to performance measurement of decision making units in data envelopment analysis concept. One of the most important shortcoming of this method is existing alternative optimal solution and therefore, the efficiency scores are not unique. We are going to summarize the pervious models proposed by researchers and suggest an alternative secondary goal approach to modify them to remove the shortcomings and difficulties of basic cross efficiency method. Also we tried the presented model to rank the efficient units.
http://ijdea.srbiau.ac.ir/article_9028_9bfc76a55d35feee16b1255ba60a0988.pdf
2015-08-01T11:23:20
2020-05-28T11:23:20
819
828
Data Envelopment Analysis
Efficiency
Cross efficiency
M.
Fallah Jelodar
true
1
AUTHOR
ORIGINAL_ARTICLE
New DEA/Location Models with Interval Data
Recently the concept of facility efficiency, which defined by data envelopment analysis (DEA), introduced as a location modeling objective, that provides facilities location’s effect on their performance in serving demands. By combining the DEA models with the location problem, two types of “efficiencies” are optimized: spatial efficiency which measured by finding the least cost location and allocation patterns for facilities, and the facility efficiency in serving demands which measured by DEA efficiency score. In this paper, location-allocation models with DEA in interval inputs and outputs environments are combined. A new pair of interval DEA/location models are constructed and run.
http://ijdea.srbiau.ac.ir/article_9029_99004c649d2c74cc5927fd16e92e528f.pdf
2015-11-01T11:23:20
2020-05-28T11:23:20
829
840
Data Envelopment Analysis
Interval DEA model
UPLP
CPLP
N.
Ghasemi
true
1
AUTHOR
E.
Najafi
true
2
AUTHOR
H.
Shams
true
3
AUTHOR
ORIGINAL_ARTICLE
Sensitivity Analysis and Finding the Stability Region with Adding DMUs in DEA
One of the important issues in data envelopment analysis (DEA) is sensitivity analysis. Heretofore the existent studies have considered the data modification of inputs and outputs in one or multiple DMUs. In this paper the number of DMUs is increased and a stability region is obtained in by applying defining hyperplanes in which if the added DMU (only one DMU) is in this region then all of the extreme efficient units will be remained on the frontier. Then it is shown that the obtained region is the largest stability region. Finally the mentioned stability region for a number of DMUs is obtained and the results are reported.
http://ijdea.srbiau.ac.ir/article_9030_87a7f83c613de5f8e7aa01223033202d.pdf
2015-11-01T11:23:20
2020-05-28T11:23:20
841
848
Data Envelopment Analysis
Sensitivity analysis
Efficiency
Hyperplane
Frontier
E.
Sarfi
true
1
AUTHOR
E.
Noroozi
true
2
AUTHOR
F.
Hosseinzadeh Lotfi
true
3
AUTHOR
ORIGINAL_ARTICLE
Differential Characteristics of Efficient Frontiers in DEA with Weight Restrictions
The non-differentiability and implicit definition of boundary of production possibility set (PPS) in data envelopment analysis (DEA) are two important difficulties for obtaining directional characteristics, including different elasticity measures and marginal rates of substitution. Also, imposing weight restrictions in DEA models have some shortcomings and misunderstandings. In this paper we utilize the core concept of directional derivative theorem to calculate different elasticity measures in DEA models with weight restrictions. Some theorems have been proved in order to overcome the problem.
http://ijdea.srbiau.ac.ir/article_9031_f361789313db83adc0f00f0e0da2cf00.pdf
2015-11-01T11:23:20
2020-05-28T11:23:20
849
856
Data Envelopment Analysis
Elasticity Measure
Directional derivative
Efficient Frontier
S.
Sohraiee
true
1
AUTHOR
ORIGINAL_ARTICLE
Utilizing Computer Simulation and DEAGP to Enhance Productivity in a Manufacturing System
Generally, a typical problem which is crucial in a manufacturing system is increasing the production rate. To cope with the problem, different types of techniques are used in companies by trial and error which imposes high costs on them. Using simulation as a tool for assessing the effect of alterations on the performance of the overall system might be significant. This paper considers a simulation based data envelopment analysis goal programming (DEAGP) applied into a well-known automobile spare part manufacturer in Kurdistan to improve production rate. The objective is to develop a simulation model based on real system to identify the imbalances and improve the performance of production system. For this purpose, in 2013 data are collected from existing system and applying full factorial design of experiments technique, different scenarios have been considered then to find the best one we used data envelopment analysis goal programming technique as a method for measuring the relative efficiency of similar units. The results show that, the optimum scenarios are 5 and 8. Applying this method could conduct us to gain more than 1% improvement in production rate using the existing resources.
http://ijdea.srbiau.ac.ir/article_9032_0fc015161a588173618c05aa586461e3.pdf
2015-11-01T11:23:20
2020-05-28T11:23:20
857
866
Computer Simulation
Production rate
Design of experiments
Data Envelopment Analysis Goal Programming
B.
Vaisi
true
1
AUTHOR
A.
Ebrahimi
true
2
AUTHOR
ORIGINAL_ARTICLE
Estimating Most Productive Scale Size with Double Frontiers in Data Envelopment Analysis using Negative Data
In this paper, it is assumed that the “Decision Making Units“( ) are consist of positive and negative input and output. Firstly, the optimistic and pessimistic models have been suggested by using negative data and then units with most productive scale size are measured in optimistic and pessimistic models. These productive values are compared with double frontiers and Hurwicz’s Criterion to obtain DMU with MPSS.
http://ijdea.srbiau.ac.ir/article_9033_21c57b34a2c22431933d80bf7466a6ab.pdf
2015-11-01T11:23:20
2020-05-28T11:23:20
867
873
Data Envelopment Analysis
Most Productive Scale Size
Optimistic efficiency
Pessimistic efficiency
double frontiers
Negative data
F.
Roozbeh
true
1
AUTHOR
R.
Eslami
true
2
AUTHOR
M.
Ahadzadeh Namin
true
3
AUTHOR