DEA Efficiency Region for Variations of Inputs and Outputs

2021 ◽  
pp. 1-26
Author(s):  
Mohammad Khoveyni ◽  
Robabeh Eslami
Keyword(s):  
Decision Making ◽  
Czech Republic ◽  
Data Envelopment Analysis ◽  
Banking Industry ◽  
Data Envelopment ◽  
The Czech Republic ◽  
Production Possibility ◽  
Dea Efficiency ◽  
Production Possibility Set ◽  
Decision Making Units

Finding efficiency regions (ERs) for extremely efficient decision-making units (DMUs) is one of the important issues from the managerial and economic viewpoints. An extremely efficient DMU will remain efficient if and only if after changing its inputs and/or its outputs this DMU stays within its ER. Thus, by applying the ER information, decision maker(s) of the evaluated extremely efficient DMU can precisely understand the values of input(s) increment and output(s) decrement of this DMU so that it remains efficient. Hence, in this study, we propose a data envelopment analysis (DEA) approach based on the defining hyperplanes of the production possibility set (PPS), which is capable of finding the ERs of the DMUs when their inputs increase and/or their outputs decrease. To demonstrate the applicability of the proposed approach, in the real world, a numerical example and an empirical application to the banking industry in the Czech Republic are provided.

New Models for Computing the Distance of DMUs to the Weak Efficient Boundary of Convex and Nonconvex PPSs in DEA

2017 ◽  
Vol 34 (06) ◽  
pp. 1750035
Author(s):  
J. Vakili
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Production Possibility ◽  
New Models ◽  
Production Possibility Set ◽  
Decision Making Units

In data envelopment analysis (DEA), calculating the distances of decision making units (DMUs) from the weak efficient boundary of a production possibility set (PPS) is a very important subject which has attracted increasing interest of researchers in recent years. The distances of DMUs to the weak efficient boundary of the PPS can be used to evaluate the performance of DMUs, obtain the closest efficient patterns and also assess the sensitivity and stability of efficiency classifications in DEA. The present study proposes some new models which compute the distances of DMUs from the weak efficient boundary of a PPS for both convex and nonconvex PPSs using Hölder norms. In fact, the presented models assist a DMU to improve its performance by an appropriate movement towards the weak efficient boundary.

A DATA ENVELOPMENT ANALYSIS (DEA) EVALUATION METHOD BASED ON SAMPLE DECISION MAKING UNITS

2010 ◽  
Vol 09 (04) ◽  
pp. 601-624 ◽  
Author(s):  
QUANLING WEI ◽  
HONG YAN
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Evaluation Method ◽  
Evaluation Procedure ◽  
Data Envelopment ◽  
Training Set ◽  
Production Possibility ◽  
Original Definition ◽  
Production Possibility Set ◽  
Decision Making Units

Most of evaluation methods on large number of candidates are based a single criterion. To bring the multiple attribute evaluation method Data Envelopment Analysis (DEA) into evaluating large number of elements, it needs to set up the performance standards and an evaluation procedure by the DEA model. In this paper, we first determine a set of "standard" candidates, called in decision making units (DMUs) in the DEA terminology. This standard set is called "training set". We then establish the evaluation procedure based on this "training set" for measuring a large number of DMUs. We first investigate the efficiency evaluation of a new DMU along with the original definition based on the sum formed production possibility set which is formed by the n DMUs in the training set and the new DMU. We then identify the intersection form of the production possibility set formed only by the n DMUs from the training set. And show that the new DMU evaluation is simply to check if the DMU satisfies a set of linear inequalities. The intersection formed production possibility set formed by the n DMUs from the training set is fixed for evaluating any new DMU. Therefore, it provides an efficient and effective method for dealing with a large amount of data. The method can be regarded as a complementary approach for data mining.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
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 ◽  
Decision Making Units

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

2012 ◽  
Vol 29 (02) ◽  
pp. 1250010 ◽  
Author(s):  
G. R. JAHANSHAHLOO ◽  
J. VAKILI ◽  
S. M. MIRDEHGHAN
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Minimum Distance ◽  
Group Performance ◽  
Returns To Scale ◽  
Data Envelopment ◽  
Production Possibility ◽  
Variable Returns To Scale ◽  
Decision Making Units

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.

scholarly journals Novel models for obtaining the closest weak and strong efficient projections in data envelopment analysis

2021 ◽  
Vol 39 (5) ◽  
pp. 9-24
Author(s):  
Javad Vakili ◽  
Hanieh Amirmoshiri ◽  
Mir Kamal Mirnia
Keyword(s):  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Efficient Frontier ◽  
Data Envelopment ◽  
Nonparametric Method ◽  
Target Setting ◽  
Production Possibility ◽  
Production Possibility Set ◽  
And Performance ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is a nonparametric method for measuring the relative efficiency and performance of Decision Making Units (DMUs). Traditionally, there are two issues regarding the DEA simultaneously i.e., the identification of a reference point on the efficient boundary of the production possibility set (PPS) and the use of some measures of distance from the unit under assessment to the efficient frontier. Due to its importance, in this paper, two alternative target setting models were developed to allow for lowefficient DMUs find the easiest way to improve its efficiency and reach to the efficient boundary. One seeks the closest weak efficient projection and the other suggests the most appropriate direction towards the strong efficient frontier surface. Both of these models provides the closest projection in one stage. Finally, a proposed problem is empirically checked by using a recent data related to 30 European airports.

scholarly journals Directional Slack-Based Measure for the Inverse Data Envelopment Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ali Mirsalehy ◽  
Mohd Rizam Abu Bakar ◽  
Lai Soon Lee ◽  
Azmi B. Jaafar ◽  
Maryam Heydar
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Efficiency Score ◽  
Data Envelopment ◽  
New Production ◽  
Production Possibility ◽  
Novel Technique ◽  
Decision Making Unit ◽  
Measure Model ◽  
Production Possibility Set

A novel technique has been introduced in this research which lends its basis to the Directional Slack-Based Measure for the inverse Data Envelopment Analysis. In practice, the current research endeavors to elucidate the inverse directional slack-based measure model within a new production possibility set. On one occasion, there is a modification imposed on the output (input) quantities of an efficient decision making unit. In detail, the efficient decision making unit in this method was omitted from the present production possibility set but substituted by the considered efficient decision making unit while its input and output quantities were subsequently modified. The efficiency score of the entire DMUs will be retained in this approach. Also, there would be an improvement in the efficiency score. The proposed approach was investigated in this study with reference to a resource allocation problem. It is possible to simultaneously consider any upsurges (declines) of certain outputs associated with the efficient decision making unit. The significance of the represented model is accentuated by presenting numerical examples.

Development of a new cost PPS and decomposition of observed actual cost for DMU in a non-competitive space in DEA

2019 ◽  
Vol 53 (5) ◽  
pp. 1563-1580
Author(s):  
Elham Rezaei Hezaveh ◽  
Reza Fallahnejad ◽  
Masoud Sanei ◽  
Mohammad Izadikhah
Keyword(s):  
Decision Making ◽  
Cost Efficiency ◽  
Allocative Efficiency ◽  
Data Envelopment ◽  
Actual Cost ◽  
Production Possibility ◽  
Production Possibility Set ◽  
The Cost ◽  
Revenue Efficiency ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is an appropriate tool for estimating various types of efficiency such as cost efficiency. There are two different sates in cost spaces; in the first space prices are equal for all Decision Making Units (DMUs) which is competitive space, and in the second space prices are different form one DMU to another; this is known as non-competitive space. The present paper introduces a new method to assess Cost Efficiency (CE), Revenue Efficiency (RE) and Profit Efficiency (PE) in a non-competitive space. The present paper also proposes a Production Possibility Set (PPS) in which DMUs are evaluated based on both their own prices and the prices of other DMUs in non-competitive space. Moreover, a new decomposition is provided for observed actual cost DMUs based on the cost efficiency model and the proposed PPS, thus the observed actual cost can be shown by summation of several technical, price and allocative efficiency (AE) losses. The biggest advantage of this method comparing to the previous methods is that passive the developed cost efficiency and the cost Production Possibility Set has been developed and the performed decomposition is more accurate; this is because the new inefficiency sources are defined and added to this new decomposition. Therefore, it includes more inefficient sources.

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

scholarly journals Measuring the Productivity of Energy Consumption of Major Industries in China: A DEA-Based Method

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xishuang Han ◽  
Xiaolong Xue ◽  
Jiaoju Ge ◽  
Hengqin Wu ◽  
Chang Su
Keyword(s):  
Decision Making ◽  
Energy Consumption ◽  
Data Envelopment Analysis ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Productivity Change ◽  
Data Envelopment ◽  
Policy Makers ◽  
Time Periods ◽  
Decision Making Units

Data envelopment analysis can be applied to measure the productivity of multiple input and output decision-making units. In addition, the data envelopment analysis-based Malmquist productivity index can be used as a tool for measuring the productivity change during different time periods. In this paper, we use an input-oriented model to measure the energy consumption productivity change from 1999 to 2008 of fourteen industry sectors in China as decision-making units. The results show that there are only four sectors that experienced effective energy consumption throughout the whole reference period. It also shows that these sectors always lie on the efficiency frontier of energy consumption as benchmarks. The other ten sectors experienced inefficiency in some two-year time periods and the productivity changes were not steady. The data envelopment analysis-based Malmquist productivity index provides a good way to measure the energy consumption and can give China's policy makers the information to promote their strategy of sustainable development.

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