SIDE

To make sound decisions, managers analyze data from multiple sources using different dimensions and eventually integrate the results of their analysis. This study proposes the design of a multi-attribute-decision-support-system that combines the analytical power of two different tools: data envelopment analysis (DEA) and particle swarm optimization (PSO), one of the major algorithms using swarm intelligence. DEA measures the relative efficiency of decision making units that use multiple inputs and outputs to provide non-objective measures without making any specific assumptions about data. On the other hand PSO's main strength lies in exploring the entire search space. This study proposes a modeling technique that jointly uses the two techniques to benefit from the two methodologies.

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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 ◽

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

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Data envelopment analysis for decision-making units: a comparison of the relative efficiency of banks in the Kingdom of Bahrain

International Journal of Economics and Business Research ◽

10.1504/ijebr.2023.10042846 ◽

2023 ◽

Vol 1 (1) ◽

pp. 1

Author(s):

Minwir Al Shammari ◽

Mohamed Sayed Abou Elseoud

Keyword(s):

Decision Making ◽

Data Envelopment Analysis ◽

Relative Efficiency ◽

Data Envelopment ◽

Decision Making Units

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Assessing most productive scale size with fuzzy stochastic data envelopment analysis

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202456 ◽

2022 ◽

pp. 1-11

Author(s):

Hooshang Kheirollahi ◽

Mahfouz Rostamzadeh ◽

Soran Marzang

Keyword(s):

Relative Efficiency ◽

Data Envelopment ◽

Random Inputs ◽

Dea Model ◽

Scale Size ◽

Stochastic Dea ◽

Probability Constraints ◽

Most Productive Scale Size ◽

Decision Making Units ◽

Fuzzy Random

Classic data envelopment analysis (DEA) is a linear programming method for evaluating the relative efficiency of decision making units (DMUs) that uses multiple inputs to produce multiple outputs. In the classic DEA model inputs and outputs of DMUs are deterministic, while in the real world, are often fuzzy, random, or fuzzy-random. Many researchers have proposed different approaches to evaluate the relative efficiency with fuzzy and random data in DEA. In many studies, the most productive scale size (mpss) of decision making units has been estimated with fuzzy and random inputs and outputs. Also, the concept of fuzzy random variable is used in the DEA literature to describe events or occurrences in which fuzzy and random changes occur simultaneously. This paper has proposed the fuzzy stochastic DEA model to assess the most productive scale size of DMUs that produce multiple fuzzy random outputs using multiple fuzzy random inputs with respect to the possibility-probability constraints. For solving the fuzzy stochastic DEA model, we obtained a nonlinear deterministic equivalent for the probability constraints using chance constrained programming approaches (CCP). Then, using the possibility theory the possibilities of fuzzy events transformed to the deterministic equivalents with definite data. In the final section, the fuzzy stochastic DEA model, proposed model, has been used to evaluate the most productive scale size of sixteen Iranian hospitals with four fuzzy random inputs and two fuzzy random outputs with symmetrical triangular membership functions.

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Detecting the multi-period performance and efficiency changes of systems with undesirable outputs

Discrete Mathematics Algorithms and Applications ◽

10.1142/s1793830918500349 ◽

2018 ◽

Vol 10 (03) ◽

pp. 1850034

Author(s):

Monireh Jahani Sayyad Noveiri ◽

Sohrab Kordrostami ◽

Alireza Amirteimoori

Keyword(s):

Relative Efficiency ◽

Weighted Average ◽

Data Envelopment ◽

Undesirable Outputs ◽

Performance Of Systems ◽

Overall Efficiency ◽

Period System ◽

Desirable And Undesirable Outputs ◽

Period Performance ◽

Decision Making Units

Data envelopment analysis (DEA) is a technique to evaluate the relative efficiency of a set of decision making units (DMUs) which is applicable in different systems such as engineering, ecology, and so forth. In real-world situations, there are instances in which production processes of systems must be analyzed in multiple periods while desirable and undesirable outputs are present; therefore, in the current paper, a DEA-based procedure is suggested to estimate the performance of systems with desirable and undesirable outputs over several periods of time. Actually, the overall and period efficiencies of DMUs in the presence of undesirable outputs are calculated by using the DEA technique. Different aspects of disposability, i.e., strong and weak, are considered for undesirable outputs. Moreover, the overall efficiency is indicated as a weighted average of the efficiencies of periods. Efficiency changes between two periods are also estimated. The proposed approach has been tested by a numerical example and applied to evaluate the efficiency of commercial transport industry in 17 countries. The findings show that efficiency scores and their changes between periods might alter by incorporating undesirable outputs into the multi-period system under evaluation; consequently, the proposed approach obtains more rational and accurate results when undesirable outputs are present.

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Measuring the Performance of Hospitals in Lebanese qadas Using PCA- DEA Model

Computer and Information Science ◽

10.5539/cis.v12n1p23 ◽

2019 ◽

Vol 12 (1) ◽

pp. 23

Author(s):

Alissar Nasser

Keyword(s):

Principal Component Analysis ◽

Relative Efficiency ◽

Principal Component ◽

Original Data ◽

Component Analysis ◽

Data Envelopment ◽

Hospital Level ◽

Discriminating Power ◽

Input Model ◽

Decision Making Units

We study in this paper the performance of Hospitals in Lebanon. Using the nonparametric method Data Envelopment Analysis (DEA), we are able to measures relative efficiency of Hospitals in Lebanon. DEA is a technique that uses linear programming and it measures the relative efficiency of similar type of organizations termed as Decision Making Units (DMUs). In this study, due to the lack of individual data on hospital level, each DMU refers to a qada in Lebanon where the used data represent the aggregation of input and outputs of different hospitals within the qada. In DEA, the inclusion of more number of inputs and /or outputs results in getting a more number of efficient units. Therefore, selecting the appropriate inputs and outputs is a major factor of DEA results. Therefore, we use here the Principal Component Analysis (PCA) in order to reduce the data structure into certain principal components which are essential for identifying efficient DMUs. It is important to note that we have used the basic BCC-input model for the entire analysis. We considered 24 DMUs for the study, using DEA on original data; we got 17 DMUs out of 24 DMUs as efficient. Then we considered 1 PC for inputs and 1 PC for output with almost 80 percent variances, resulting in 3 DMUs as efficient and 21 as inefficient. Using 1 PC for input and 2 PCs for output with 90 percent variance for both input and output, we got 9 DMUs as efficient and 15 DMUs as inefficient. Finally, we have attempted to identify the efficient units with 2 PCs and for 2 PCs for input and outputs with variance more than 95 percent, resulting in 10 efficient DMUs and 14 inefficient DMUs. In Principal Component analysis, if the variance lies between 80 percent to-90 percent it is judged as a meaningful one. It is concluded that Principal Component Analysis plays an important role in the reduction of input output variables and helps in identifying the efficient DMUs and improves the discriminating power of DEA.

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Alternative Secondary Goals in Multiplicative Two-Stage Data Envelopment Analysis

Mathematical Problems in Engineering ◽

10.1155/2021/9931796 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Chao Lu ◽

Haifang Cheng

Keyword(s):

Data Envelopment Analysis ◽

Relative Efficiency ◽

Data Envelopment ◽

Nonparametric Method ◽

Two Stage ◽

Dea Model ◽

Second Stage ◽

China Construction Bank ◽

Overall Efficiency ◽

Decision Making Units

Data envelopment analysis (DEA) is a nonparametric method for evaluating the relative efficiency of a set of decision-making units (DMUs) with multiple inputs and outputs. As an extension of the DEA, a multiplicative two-stage DEA model has been widely used to measure the efficiencies of two-stage systems, where the first stage uses inputs to produce the outputs, and the second stage then uses the first-stage outputs as inputs to generate its own outputs. The main deficiency of the multiplicative two-stage DEA model is that the decomposition of the overall efficiency may not be unique because of the presence of alternate optima. To remove the problem of the flexible decomposition, in this paper, we maximize the sum of the two-stage efficiencies and simultaneously maximize the two-stage efficiencies as secondary goals in the multiplicative two-stage DEA model to select the decomposition of the overall efficiency from the flexible decompositions, respectively. The proposed models are applied to evaluate the performance of 10 branches of China Construction Bank, and the results are compared with the results of the existing models.

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Evaluation of Supplier Performance and Efficiency

Data Envelopment Analysis and Effective Performance Assessment - Advances in Business Information Systems and Analytics ◽

10.4018/978-1-5225-0596-9.ch005 ◽

2017 ◽

pp. 184-216

Author(s):

Chandra Sekhar Patro

Keyword(s):

Decision Making ◽

Data Envelopment Analysis ◽

Relative Efficiency ◽

Efficient Frontier ◽

Business Environment ◽

Vital Role ◽

Data Envelopment ◽

Supplier Performance ◽

Decision Making Units

In the present competitive business environment, it is essential for the management of any organisation to take wise decisions regarding supplier evaluation. It plays a vital role in establishing an effective supply chain for any organisation. Most of the experts agreed that there is no one best way to evaluate the suppliers and different organizations use different approaches for evaluating supplier efficiency. The overall objective of any approach is to reduce purchase risk and maximize overall value to the purchaser. In this paper Data Envelopment Analysis (DEA) technique is developed to evaluate the supplier efficiency for an organisation. DEA is a multifactor productivity technique to measure the relative efficiency of the decision making units. The super efficiency method of DEA provides a way, which indicates the extent to which the efficient suppliers exceed the efficient frontier formed by other efficient suppliers. A case study is undertaken to evaluate the supplier performance and efficiency using DEA approach.

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Ranking DMUs by Calculating the Interval Efficiency with a Common Set of Weights in DEA

Journal of Applied Mathematics ◽

10.1155/2014/346763 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Vahideh Rezaie ◽

Tahir Ahmad ◽

Siti-Rahmah Awang ◽

Masumeh Khanmohammadi ◽

Normah Maan

Keyword(s):

Decision Maker ◽

Relative Efficiency ◽

Efficiency Score ◽

Upper Bounds ◽

Data Envelopment ◽

Lower And Upper Bounds ◽

Common Set Of Weights ◽

Overall Performance ◽

Interval Efficiency ◽

Decision Making Units

To evaluate the performance of decision making units (DMUs), data envelopment analysis (DEA) was introduced. Basically, the traditional DEA scheme calculates the best relative efficiency score (i.e., the “optimistic” efficiency) of each DMU with the most favorable weights. A decision maker may be unable to compare and fully rank the efficiencies of different DMUs that are calculated using these potentially distinct sets of weights on the same basis. Based on the literature, the assignable worst relative efficiency score (i.e., the “pessimistic” efficiency) for each DMU can also be determined. In this paper, the best and the worst relative efficiencies are considered simultaneously. To measure the overall performance of the DMUs, an integration of both the best and the worst relative efficiencies is considered in the form of an interval. The advantage of this efficiency interval is that it provides all of the possible efficiency values and an expanded overview to the decision maker. The proposed method determines the lower- and upper-bounds of the interval efficiency over a common set of weights. To demonstrate the implementation of the introduced method, a numerical example is provided.

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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 ◽

Data Envelopment Analysis ◽

Technical Efficiency ◽

Relative Efficiency ◽

Fuel Efficiency ◽

Returns To Scale ◽

Road Transport ◽

Data Envelopment ◽

Variable Returns To Scale ◽

Decision Making Units

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.

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