Construction of pseudo CRS frontier for a negative data using RTS model of Allahyar & modified multiplier BCC model

Performance measurement of Decision Making Units (DMU) possessing an array of positive and negative type of data has been an extensively researched topic in Data Envelopment Analysis. However, assessment of Returns to Scale (RTS) under negative data problem is rarely witnessed without the steps referred by Allahyar, M. (2015). Authors purported a solution around the vicinity of the Decision Making Unit under examination to predict the nature of the Return to Scale of a firm. The extant investigation is aimed to extend the research of Allahyar, M. (2015) to identify a Pseudo Frontier for a negative data problem under Constant Return to Scale. In addition to it, a new origin based on the provided data is also computed with a view to convert the entire data set into a positive dataset. However, this approach seems to be ineffective to create a frontier under the multiple input output scenario. In this regard, a new variation of the Multiplier form of BCC model is proposed here to detect the new origin for the sake of designing the Pseudo CRS Frontier. Small examples are added for the elaboration of the CRS efficient DMUs using methods described by Allahyar, M. (2015) and identification of the New Origin from the Multiplier form of BCC model.

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

The Pakistan Development Review ◽

10.30541/v50i4iipp.685-698 ◽

2011 ◽

Vol 50 (4II) ◽

pp. 685-698

Author(s):

Samina Khalil

Keyword(s):

Decision Making ◽

Relative Efficiency ◽

Value Added ◽

Data Envelopment ◽

Undesirable Outputs ◽

Textile Processing ◽

Decision Making Unit ◽

Desirable And Undesirable Outputs ◽

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

Forecast of the efficiency confidence interval of decision-making units in data envelopment analysis

Nexo Revista Científica ◽

10.5377/nexo.v33i02.10782 ◽

2020 ◽

Vol 33 (02) ◽

pp. 431-445

Author(s):

Azarnoosh Kafi ◽

Behrouz Daneshian ◽

Mohsen Rostamy-Malkhalifeh ◽

Mohsen Rostamy-Malkhalifeh

Keyword(s):

Decision Making ◽

Confidence Interval ◽

Data Envelopment ◽

Data Set ◽

Interval Model ◽

Time Period ◽

Proposed Model ◽

The Future ◽

Data Envelopment Analysis (DEA) is a well-known method for calculating the efficiency of Decision-Making Units (DMUs) based on their inputs and outputs. When the data is known and in the form of an interval in a given time period, this method can calculate the efficiency interval. Unfortunately, DEA is not capable of forecasting and estimating the efficiency confidence interval of the units in the future. This article, proposes a efficiency forecasting algorithm along with 95% confidence interval to generate interval data set for the next time period. What’s more, the manager’s opinion inserts and plays its role in the proposed forecasting model. Equipped with forecasted data set and with respect to data set from previous periods, the efficiency for the future period can be forecasted. This is done by proposing a proposed model and solving it by the confidence interval method. The proposed method is then implemented on the data of an automotive industry and, it is compared with the Monte Carlo simulation methods and the interval model. Using the results, it is shown that the proposed method works better to forecast the efficiency confidence interval. Finally, the efficiency and confidence interval of 95% is calculated for the upcoming period using the proposed model.

A Full Ranking for Decision Making Units Using Ideal and Anti-Ideal Points in DEA

The Scientific World JOURNAL ◽

10.1155/2014/282939 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 6

Author(s):

A. Barzegarinegad ◽

G. Jahanshahloo ◽

M. Rostamy-Malkhalifeh

Keyword(s):

Decision Making ◽

Real Data ◽

The Other ◽

Data Envelopment ◽

Production Possibility ◽

Decision Making Unit ◽

Common Set Of Weights ◽

Production Possibility Set ◽

Ideal Points ◽

We propose a procedure for ranking decision making units in data envelopment analysis, based on ideal and anti-ideal points in the production possibility set. Moreover, a model has been introduced to compute the performance of a decision making unit for these two points through using common set of weights. One of the best privileges of this method is that we can make ranking for all decision making units by solving only three programs, and also solving these programs is not related to numbers of decision making units. One of the other advantages of this procedure is to rank all the extreme and nonextreme efficient decision making units. In other words, the suggested ranking method tends to seek a set of common weights for all units to make them fully ranked. Finally, it was applied for different sets holding real data, and then it can be compared with other procedures.

USING THE MINIMUM DISTANCE OF DMUs FROM THE FRONTIER OF THE PPS FOR EVALUATING GROUP PERFORMANCE OF DMUs IN DEA

Asia Pacific Journal of Operational Research ◽

10.1142/s0217595912500108 ◽

2012 ◽

Vol 29 (02) ◽

pp. 1250010 ◽

Cited By ~ 11

Author(s):

G. R. JAHANSHAHLOO ◽

J. VAKILI ◽

S. M. MIRDEHGHAN

Keyword(s):

Decision Making ◽

Minimum Distance ◽

Group Performance ◽

Returns To Scale ◽

Data Envelopment ◽

Production Possibility ◽

Variable Returns To Scale ◽

Evaluating group performance of decision-making units (DMUs) is an application of data envelopment analysis (DEA) and usually provides a measure to compare the frontiers of the production possibility sets (PPSs) corresponding to different groups and the internal inefficiencies of DMUs associated with their group. In this paper, first, a method is presented for obtaining the minimum distance of DMUs from the frontier of the PPS by ‖⋅‖1, which itself can be a very important subject in DEA, and then, for stating an application of these distances, an approach is provided for evaluating group performance of DMUs based on the production ability of the PPSs such that both constant and variable returns to scale assumptions can be used in this method in contrast with some other methods. Therefore, providing the methods for both obtaining the minimum distance of DMUs from the frontier of the PPS and evaluating group performance of DMUs is the most important contribution of this paper.

An algorithm for the anchor points of the PPS of the FRH models

RAIRO - Operations Research ◽

10.1051/ro/2020086 ◽

2020 ◽

Author(s):

Dariush Akbarian

Keyword(s):

Decision Making ◽

Sufficient Conditions ◽

Data Envelopment ◽

Proposed Model ◽

Decision Making Unit ◽

Production Possibility Set ◽

Anchor Points ◽

Definition Of ◽

Necessary And Sufficient ◽

In this paper we deal with a variant of non-convex data envelopment analysis, called free replication hull model and try to obtain their anchor points. This paper uses a variant of super-efficiency model to characterize all extreme efficient decision making units and anchor points of the free replication hull models. A necessary and sufficient conditions for a decision making unit to be anchor point of the production possibility set of the free replication hull models are stated and proved. Since the set of anchor points is a subset of the set of extreme units, a definition of extreme units and a new method for obtaining these units in non-convex technologies are given. To illustrate the applicability of the proposed model, some numerical examples are finally provided.

Global malmquist index for measuring the economic productivity changes

Measurement and Control ◽

10.1177/0020294020923096 ◽

2020 ◽

Vol 53 (7-8) ◽

pp. 1278-1285

Author(s):

Esmaeil Mombini ◽

Mohsen Rostamy-Malkhalifeh ◽

Mansor Saraj ◽

Mohsen Zahraei ◽

Reza Tayebi Khorami

Keyword(s):

Decision Making ◽

Malmquist Index ◽

Productivity Index ◽

Programming Models ◽

Data Envelopment ◽

Decision Unit ◽

Economic Productivity ◽

Use Of Resources ◽

Decision Making Unit ◽

Data envelopment analysis is a nonparametric method for measuring of the performance of decision-making units—which do not need to have or compute a firm’s production function, which is often difficult to calculate. For any manager, the progress or setback of the thing they manage is important because it makes planning and adoption of future policies for the organization or decision-making unit more rational and scientific. Different methods have been used to calculate the improvements and regressions using Malmquist Index. In this article, we evaluate the units under review in terms of economic efficiency, and the units in terms of spending, production, revenue and profit over several periods, and the rate of improvement or regression of each of these units. Considering the minimal use of resources and consuming less money, generating more revenue, and maximizing profits, the improvement or retreat of the recipient’s decision unit in terms of cost, revenue, and profit was examined by presenting a method based on solving linear programming models using the productivity index is Malmquist and Malmquist Global. Finally, by designing and solving a numerical example, we emphasize and test the applicability of the material presented in this article.

Comparing Data Envelopment Analysis and Human Decision Making Unit Rankings: A Survey Approach

Economic Quality Control ◽

10.1515/eqc-2014-0013 ◽

2014 ◽

Vol 29 (2) ◽

Author(s):

Stanko Dimitrov

Keyword(s):

Decision Making ◽

Decision Makers ◽

Data Envelopment ◽

Rank Ordering ◽

Decision Making Unit ◽

Human Decision ◽

Survey Approach ◽

Decision Making Units ◽

Better Than

AbstractIn this paper we compare the ordinal rankings generated through Data Envelopment Analysis (DEA) methods to ordinal rankings generated by human decision makers. Through eliciting the total rank ordering for approximately 100 individuals on all of the four different datasets of Decision Making Units (DMUs), we compare the rankings generated by individuals to those generated by ten DEA methods. We observe that depending on the characteristics of the dataset one of the DEA methods performs better than the others in matching human decision makers.

Technical Efficiency of State Road Transport Undertakings in India - Data Envelopment Analysis

International Journal of Productivity Management and Assessment Technologies ◽

10.4018/ijpmat.2012040103 ◽

2012 ◽

Vol 1 (2) ◽

pp. 31-42 ◽

Cited By ~ 1

Author(s):

V. Prakash ◽

J. Rajesh ◽

M. Thilagam

Keyword(s):

Decision Making ◽

Technical Efficiency ◽

Relative Efficiency ◽

Fuel Efficiency ◽

Returns To Scale ◽

Road Transport ◽

Data Envelopment ◽

Variable Returns To Scale ◽

Data envelopment analysis (DEA) is a method of analyzing the relative efficiency of similar types of organizations known as decision making units (DMU’s). In this paper, DEA model is applied to evaluate the relative technical efficiency of state road transport undertakings (SRTU’s) in India during the period 2011-2012. The authors have considered thirty-four SRTU’s functioning in India. The variables chosen to characteristic production units are the number of fleet held, staff strength and fuel efficiency as inputs and Passengers carried as output. The BCC model is input- oriented allowing for variable returns to scale (VRS), units are ranked and the projection analyses are given.

A multiobjective approach for non-discretionary variables in data envelopment analysis

DYNA ◽

10.15446/dyna.v83n195.45396 ◽

2016 ◽

Vol 83 (195) ◽

pp. 9-15 ◽

Cited By ~ 3

Author(s):

Lidia Angulo Meza ◽

João Carlos Soares de Mello ◽

Silvio Gomes Junior

Keyword(s):

Decision Making ◽

Decision Maker ◽

Data Envelopment ◽

Efficiency Evaluation ◽

Data Set ◽

Numerical Example ◽

Multiobjective Approach ◽

Ratio Optimization ◽

Data Envelopment Analysis is a non-parametrical approach for efficiency evaluation of so-called DMUs (Decision Making Units) and takes into account multiple inputs and outputs. For each inefficient DMU, a target is provided which is constituted by the inputs or outputs levels that are to be attained for the inefficient DMU to become efficient. However, multiobjective models, known as MORO (Multiobjective Model for Ratio Optimization) provide a set of targets for inefficient DMU, which provides alternatives among which the decision-maker can choose. In this paper, we proposed an extension of the MORO models to take into account non-discretionary variables, i.e., variables that cannot be controlled. We present a numerical example to illustrate the proposed multiobjective model. We also discuss the characteristics of this model, as well as the advantages of offering a set of targets for the inefficient DMUs when there are non-discretionary variables in the data set.

Analyzing the efficiency bank branches via novel weighted stochastic imprecise data envelopment analysis

RAIRO - Operations Research ◽

10.1051/ro/2021067 ◽

2021 ◽

Author(s):

Nezir Aydın ◽

Gökhan Yurdakul

Keyword(s):

Decision Making ◽

Sensitivity Analyses ◽

Data Envelopment ◽

Imprecise Data ◽

Data Set ◽

Dea Model ◽

Imprecise Data Envelopment Analysis ◽

Efficiency And Productivity ◽

As of 21 th century, the terms of efficiency and productivity have become notions which dwells on both business and academic world more frequently compared to past. It is known that it is hard to increase the efficiency and productivity of both production and service systems. In this study, the efficiency analysis of the branches of a bank was conducted. Furthermore, a Weighted Stochastic Imprecise Data Envelopment Analysis (WSIDEA), which is a new approach developed based on Data Envelopment Analysis (DEA), was proposed. Efficiency levels and results of decision-making units were examined according to the proposed new method. Additionally, six different DEA model results are obtained. The results of the six different DEA model and the proposed "WSIDEA" model were compared in terms of efficiency level of decision-making units, and the differences between them were examined. Sensitivity of the inefficient units were also examined. On the other hand, unrealistic efficiency levels created by traditional methods for branches were also analyzed. Apart from all these sensitivity analyses, the sensitivity of the data set used in the analysis is scrutinized.