Classifying Inputs and Outputs in Data Envelopment Analysis Based on TOPSIS Method and a Voting Model

2014 ◽  
Vol 1 (2) ◽  
pp. 48-63 ◽  
Author(s):  
M. Soltanifar ◽  
S. Shahghobadi
Keyword(s):  
Data Envelopment Analysis ◽  
Programming Model ◽  
Performance Measure ◽  
Point Of View ◽  
Data Envelopment ◽  
Topsis Method ◽  
The Status ◽  
Decision Making Units ◽  
Inputs And Outputs ◽  
Voting Model

In conventional data envelopment analysis, it is assumed that the input versus output status of any particular performance measure is known. In some situations, finding the status of some variables from the input or output point of view is very difficult; these variables are treated as both inputs and outputs and are called flexible measures. In this paper, using the TOPSIS method, and also using a voting model, the status of such a variable will be determined, and the results obtained will be employed to evaluate the efficiency of homogeneous decision making units. Note that all the models used in this paper are linear programming models and there is no need to solve any integer programming model. The approach is illustrated by an example.

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.

Restricted variation in data envelopment analysis with undesirable factors in nature

2015 ◽  
Vol 08 (03) ◽  
pp. 1550034 ◽  
Author(s):  
Sohrab Kordrostami ◽  
Alireza Amirteimoori ◽  
Monireh Jahani Sayyad Noveiri
Keyword(s):  
Data Envelopment Analysis ◽  
Efficient Frontier ◽  
Data Envelopment ◽  
Standard Data ◽  
Weak Disposability ◽  
Dea Models ◽  
Restricted Environment ◽  
Restricted Variation ◽  
Decision Making Units ◽  
Inputs And Outputs

In standard data envelopment analysis (DEA) models, inefficient decision-making units (DMUs) should change their inputs and outputs arbitrarily to meet the efficient frontier. However, in many real applications of DEA, because of some limitations in resources and DMU's ability, these variations cannot be made arbitrarily. Moreover, in some situations, undesirable factors with different disposability, strong or weak disposability, are found. In this paper, a DEA-based model is proposed to determine the relative efficiency of DMUs in such a restricted environment and in presence of undesirable factors. Indeed, variation levels of inputs and outputs are pre-defined and are considered to evaluate the performance of DMUs. Numerical examples are utilized to demonstrate the approach.

Performance Measurement

2011 ◽  
pp. 709-715 ◽  
Author(s):  
João Carlos Namorado Clímaco ◽  
João Carlos Soares de Mello ◽  
Lidia Angulo Meza
Keyword(s):  
Raw Materials ◽  
Performance Measure ◽  
Point Of View ◽  
Decision Makers ◽  
The Other ◽  
Computational Technique ◽  
Data Envelopment ◽  
Multi Objective ◽  
Input And Output ◽  
Decision Making Units

Data envelopment analysis (DEA) is a non-parametric technique to measure the efficiency of productive units as they transform inputs into outputs. A productive unit has, in DEA terms, an all-encompassing definition. It may as well refer to a factory whose products were made from raw materials and labor or to a school that, from prior knowledge and lessons time, produces more knowledge. All these units are usually named decision making units (DMU). So, DEA is a technique enabling the calculation of a single performance measure to evaluate a system. Although some DEA techniques that cater for decision makers’ preferences or specialists’ opinions do exist, they do not allow for interactivity. Inversely, interactivity is one of the strongest points of many of the multi-criteria decision aid (MCDA) approaches, among which those involved with multi-objective linear programming (MOLP) are found. It has been found for several years that those methods and DEA have several points in common. So, many works have taken advantage of those common points to gain insight from a point of view as the other is being used. The idea of using MOLP, in a DEA context, appears with the Pareto efficiency concept that both approaches share. However, owing to the limitations of computational tools, interactivity is not always fully exploited. In this article we shall show how one, the more promising model in our opinion that uses both DEA and MOLP (Li & Reeves, 1999), can be better exploited with the use of TRIMAP (Climaco & Antunes, 1987, 1989). This computational technique, owing in part to its graphic interface, will allow the MCDEA method potentialities to be better used. MOLP and DEA share several concepts. To avoid naming confusion, the word weights will be used for the weighing coefficients of the objective functions in the multi-objective problem. For the input and output coefficients the word multiplier shall be used. Still in this context, the word efficient shall be used only in a DEA context and, for the MOLP problems, the optimal Pareto solutions will be called non-dominated solutions.

Energy cost versus production as a performance benchmark for analysis of companies

2013 ◽  
Vol 24 (1) ◽  
pp. 2-8
Author(s):  
Houshang Taghizadeh ◽  
Mir Vahid Pourrabbi
Keyword(s):  
Energy Efficiency ◽  
Data Envelopment Analysis ◽  
Automotive Industry ◽  
Energy Cost ◽  
Programming Model ◽  
Data Envelopment ◽  
Performance Benchmark ◽  
Auto Parts ◽  
Using Data ◽  
Decision Making Units

In this paper, the efficiency of automobile and auto-parts producing companies is evaluated, using data envelopment analysis. The data envelopment analysis (DEA) is based on the linear programming model. This model needs a series of information by which the effectiveness and ineffectiveness of decision-making units are determined. In the present research, the inputs and outputs of the DEA model are determined by using the basic criteria, and the energy efficiency of automobile and auto-parts producing companies is specified. For this purpose, to evaluate the energy efficiency of the automotive industry and auto parts producing companies, deep2 software has been used. Finally, based on the results of the study, the efficient and inefficient companies have been identified and classified.

A Non-Radial Input Relaxation Model Approach for Evaluating Input Congestion in Data Envelopment Analysis

2015 ◽  
Vol 5 (3) ◽  
pp. 45-60
Author(s):  
M. Khodabakhshi ◽  
H. Kheirollahi ◽  
M. Rezaee ◽  
Barnett R. Parker
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Additive Model ◽  
Point Of View ◽  
Data Envelopment ◽  
Relaxation Model ◽  
Computational Point ◽  
Radial Model ◽  
Decision Making Units ◽  
Model Approach

This paper develops a non-radial model, unified additive model approach, for evaluating decision making units in data envelopment analysis. Based on the proposed additive input relaxation model, two approaches, a two model approach and a one model approach are provided for determining input congestion. It is shown that the proposed two model approach determines the amount of congestion and, simultaneously, identifies factors responsible for congestion and distinguishes congestion amounts from other components of inefficiency. These amounts are all obtainable from non-zero slacks in a slightly altered version of the additive input relaxation model which we further extend and modify to obtain additional details. One model approach, also, determines input congestion, and is preferred from computational point of view. Numerical and empirical examples are used to illustrate the proposed non-radial methods.

scholarly journals Sampling size and efficiency bias in data envelopment analysis

1998 ◽  
Vol 2 (1) ◽  
pp. 51-64 ◽  
Author(s):  
Mohammad R. Alirezaee ◽  
Murray Howland ◽  
Cornelis van de Panne
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Large Scale ◽  
Returns To Scale ◽  
Data Envelopment ◽  
Bank Branch ◽  
Decision Making Units ◽  
Inputs And Outputs

In Data Envelopment Analysis, when the number of decision making units is small, the number of units of the dominant or effcient set is relatively large and the average effciency is generally high. The high average effciency is the result of assuming that the units in the effcient set are 100% effcient. If this assumption is not valid, this results in an overestimation of the efficiencies, which will be larger for a smaller number of units. Samples of various sizes are used to find the related bias in the effciency estimation. The samples are drawn from a large scale application of DEA to bank branch efficiency. The effects of different assumptions as to returns to scale and the number of inputs and outputs are investigated.

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 Performance Evaluation of Fishery Enterprises Using Data Envelopment Analysis—A Malmquist Model

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 469
Author(s):  
Chia-Nan Wang ◽  
Thi-Ly Nguyen ◽  
Thanh-Tuan Dang ◽  
Thi-Hong Bui
Keyword(s):  
Performance Evaluation ◽  
Data Envelopment Analysis ◽  
Technological Progress ◽  
Data Envelopment ◽  
Domestic Economy ◽  
Factor Productivity ◽  
Sales Revenue ◽  
Business Efficiency ◽  
Using Data ◽  
Decision Making Units

In Vietnam, fishing is a crucial source of nutrition and employment, which not only affects the development of the domestic economy but is also closely related to exports, heavily influencing the economy and foreign exchange. However, the Vietnamese fishery sector has been facing many challenges in innovating production technology, improving product quality, and expanding markets. Hence, the fishery enterprises need to find solutions to increase labor productivity and enhance competitiveness while minimizing difficulties. This study implemented a performance evaluation from 2015 to 2018 of 17 fishery businesses, in decision making units (DMUs), in Vietnam by applying data envelopment analysis, namely the Malmquist model. The objective of the paper is to provide a general overview of the fishery sector in Vietnam through technical efficiency, technological progress, and the total factor productivity in the four-year period. The variables used in the model include total assets, equity, total liabilities, cost of sales, revenue, and profit. The results of the paper show that Investment Commerce Fisheries Corporation (DMU10) and Hoang Long Group (DMU8) exhibited the best performances. This paper offers a valuable reference to improve the business efficiency of Vietnamese fishery enterprises and could be a useful reference for related industries.

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