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

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.

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

A Common Set of Weights for Ranking Decision-Making Units with Undesirable Outputs: A Double Frontiers Data Envelopment Analysis Approach

2018 ◽  
Vol 35 (06) ◽  
pp. 1850039 ◽  
Author(s):  
Lei Chen ◽  
Fei-Mei Wu ◽  
Feng Feng ◽  
Fujun Lai ◽  
Ying-Ming Wang
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Optimal Solution ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Discrimination Power ◽  
Efficiency Optimization ◽  
Common Set Of Weights ◽  
Desirable And Undesirable Outputs ◽  
Decision Making Units

Major drawbacks of the traditional data envelopment analysis (DEA) method include selecting optimal weights in a flexible manner, lacking adequate discrimination power for efficient decision-making units, and considering only desirable outputs. By introducing the concept of global efficiency optimization, this study proposed a double frontiers DEA approach with undesirable outputs to generate a common set of weights for evaluating all decision-making units from both the optimistic and pessimistic perspectives. For a unique optimal solution, compromise models for individual efficiency optimization were developed as a secondary goal. Finally, as an illustration, the models were applied to evaluate the energy efficiency of the Chinese regional economy. The results showed that the proposed approach could improve discrimination power and obtain a fair result in a case where both desirable and undesirable outputs exist.

scholarly journals Alternative Secondary Goals in Multiplicative Two-Stage Data Envelopment Analysis

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.

Assessing most productive scale size with fuzzy stochastic data envelopment analysis

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.

SIDE

2014 ◽  
Vol 5 (1) ◽  
pp. 39-58 ◽  
Author(s):  
Rashmi Malhotra
Keyword(s):  
Relative Efficiency ◽  
Search Space ◽  
Objective Measures ◽  
Data Envelopment ◽  
Multiple Sources ◽  
Swarm Optimization ◽  
Different Dimensions ◽  
Analytical Power ◽  
Decision Making Units ◽  
Analyze Data

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.

scholarly journals A Revised Inverse Data Envelopment Analysis Model Based on Radial Models

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 803
Author(s):  
Xiaoyin Hu ◽  
Jianshu Li ◽  
Xiaoya Li ◽  
Jinchuan Cui
Keyword(s):  
Data Envelopment Analysis ◽  
Efficiency Index ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
Numerical Examples ◽  
Wide Range ◽  
Dea Models ◽  
Overall Efficiency ◽  
Decision Making Units

In recent years, there has been an increasing interest in applying inverse data envelopment analysis (DEA) to a wide range of disciplines, and most applications have adopted radial-based inverse DEA models. However, results given by existing radial based inverse DEA models can be unreliable as they neglect slacks while evaluating decision-making units’ (DMUs) overall efficiency level, whereas classic radial DEA models measure the efficiency level through not only radial efficiency index but also slacks. This paper points out these disadvantages with a counterexample, where current inverse DEA models give results that outputs shall increase when inputs decrease. We show that these unreasonable results are the consequence of existing inverse DEA models’ failure in preserving DMU’s efficiency level. To rectify this problem, we propose a revised model for the situation where the investigated DMU has no slacks. Compared to existing radial inverse DEA models, our revised model can preserve radial efficiency index as well as eliminating all slacks, thus fulfilling the requirement of efficiency level invariant. Numerical examples are provided to illustrate the validity and limitations of the revised model.

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

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.

DEA based production planning considering technology heterogeneity with undesirable outputs

2020 ◽  
Vol 54 (2) ◽  
pp. 325-339
Author(s):  
Changyong Liang ◽  
Binyou Wang ◽  
Tao Ding ◽  
Yinchao Ma
Keyword(s):  
Data Envelopment Analysis ◽  
Production Planning ◽  
Industrial Development ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Planning Approach ◽  
Technology Heterogeneity ◽  
Using Data ◽  
Planning Problems ◽  
Decision Making Units

Many researchers have concentrated on production planning issues by using data envelopment analysis (DEA). However, the assumption made by existing approaches that all decision making units (DMUs) are equipped with the same level of production technology is not realistic. Additionally, with the development in the society, environmental factors have come to play important roles in the production process as well. Thus, undesirable outputs should be considered in production planning problems. Therefore, this paper considers the technology heterogeneity factors and undesirable outputs using the data envelopment analysis-based production planning approach. Two examples containing a numerical example that compare with other method and a real sample that concerns the industrial development of 30 provinces in China are used to validate the applicability of our approach.

Evaluation of Supplier Performance and Efficiency

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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