scholarly journals DEA-BASED INTEGRATED RELATIONSHIP OF RETURNS TO SCALE – AN APPLICATION TO ROAD MAINTENANCE IN TAIWAN

2012 ◽  
Vol 18 (5) ◽  
pp. 709-723 ◽  
Author(s):  
Gang Lee ◽  
Ming-Miin Yu ◽  
Lung-Chuang Wang
Keyword(s):  
Returns To Scale ◽  
Data Envelopment ◽  
Road Maintenance ◽  
Highway Maintenance ◽  
Multi Stage ◽  
The Status ◽  
Directorate General ◽  
Relationship Of ◽  
Constant Returns To Scale ◽  
Decision Making Units

An Integrated Relationship of Returns to Scale (IRRS) associated with multiple-stage Data Envelopment Analysis (DEA) is proposed for identifying the returns to scale (RTS) among decision-making units (DMUs) appropriately and accurately. The validity and feasibility of the proposed method is tested by using 31 case studies on highway maintenance and construction offices based on the data provided by the Directorate General of Highways (Taiwan). The results show that the multi-stage DEA method with IRRS is more useful than the traditional single-stage DEA for evaluating the status of RTS for each DMU. Among the 31 units evaluated, 14 units are categorized as having increasing returns to scale, 4 have decreasing returns to scale, and no unit has constant returns to scale; the returns for the remaining 13 units cannot be determined.

scholarly journals An Entropy-Based Cross-Efficiency under Variable Returns to Scale

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1205
Author(s):  
Chun-Hsiung Su ◽  
Tim Lu
Keyword(s):  
Evaluation Method ◽  
Returns To Scale ◽  
Peer Evaluation ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Dea Models ◽  
Constant Returns To Scale ◽  
Numerical Index ◽  
Variable Returns To Scale ◽  
Decision Making Units

Cross-efficiency evaluation is an effective methodology for discriminating among a set of decision-making units (DMUs) through both self- and peer-evaluation methods. This evaluation technique is usually used for data envelopment analysis (DEA) models with constant returns to scale due to the fact that negative efficiencies never happen in this case. For cases of variable returns to scale (VRSs), the evaluation may generate negative cross-efficiencies. However, when the production technology is known to be VRS, a VRS model must be used. In this case, negative efficiencies may occur. Negative efficiencies are unreasonable and cause difficulties in calculating the final cross-efficiency. In this paper, we propose a cross-efficiency evaluation method, with the technology of VRS. The cross-efficiency intervals of DMUs were derived from the associated aggressive and benevolent formulations. More importantly, the proposed approach does not produce negative efficiencies. For comparison of DMUs with their cross-efficiency intervals, a numerical index is required. Since the concept of entropy is an effective tool to measure the uncertainty, this concept was employed to build an index for ranking DMUs with cross efficiency intervals. A real-case example was used to illustrate the approach proposed in this paper.

Data Envelopment Analysis with Output-Bounded Data

2016 ◽  
Vol 33 (06) ◽  
pp. 1650050 ◽  
Author(s):  
Juan Du ◽  
Jiazhen Huo ◽  
Joe Zhu
Keyword(s):  
Data Envelopment Analysis ◽  
Returns To Scale ◽  
Data Envelopment ◽  
Practical Applications ◽  
Efficiency Measures ◽  
Output Side ◽  
Bounded Data ◽  
Constant Returns To Scale ◽  
Percentage Data ◽  
Decision Making Units

In conventional data envelopment analysis (DEA), data are usually assumed to be non-negative with no specific bounds. However, many practical applications require some data, and thus their projections, to fall within certain limits. For example, percentage data such as the satisfactory rate cannot exceed 100% to make sense. This data characteristic is very likely to be violated under the assumption of constant returns to scale (CRS), due to its ray expansion property. In order to tackle this issue under CRS, a series of radial models are developed to constrain DEA projections within imposed bounds from the output side. Then efficient decision making units (DMUs) can be further discriminated simply by eliminating it from the reference set, avoiding the infeasibility problem existing in the VRS super-efficiency measures. The methodology is demonstrated with data consisting of 119 general acute care hospitals located in Pennsylvania, USA.

scholarly journals Energy Requirement Optimization of Greenhouse Vegetable Production Using Data Envelopment Analysis (DEA) Method in Algeria

2020 ◽  
Vol 23 (2) ◽  
pp. 60-66
Author(s):  
Ahmed Nourani ◽  
Abdelaali Bencheikh
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Returns To Scale ◽  
Scale Model ◽  
Energy Requirements ◽  
Data Envelopment ◽  
Greenhouse Vegetable ◽  
Constant Returns To Scale ◽  
Variable Returns To Scale ◽  
Decision Making Units

AbstractAlgeria has recently experienced an important agricultural development in terms of gardening in plastic greenhouses thanks to the favourable factors (climatic conditions, etc.). In order to optimize the energy requirements, data from 29 farmers were collected, who qualitatively represent the greenhouse vegetable producers from the most productive sub-provinces of Biskra region (south of Algeria). Considering the various parametric and non-parametric methods for energy consumption optimization, data envelopment analysis is the most common non-parametric method applied. Results showed that the mean radial technical efficiency assumptions of the samples under constant returns to scale and variable returns to scale models were 0.88 and 0.98, respectively. The 51.72% of decision-making units were efficient on the basis of the constant returns to scale model; 79.31% decision-making units were observed efficient on the basis of variable returns to scale model. Calculation of optimal energy requirements for vegetable greenhouse indicated that 108.50 GJ·ha−1 can be saved on machinery (1.38 GJ·ha-1); diesel fuel (4.68 GJ·ha−1); infrastructure (9.35 GJ·ha−1); fertilizers (17.08 GJ·ha−1); farmyard manure (12.05 GJ·ha−1); pesticides (3.93 GJ·ha−1); and electricity (60.03 GJ·ha−1).

scholarly journals Applying data envelopment analysis to operating performance in textile industry

2013 ◽  
Vol 13 (4) ◽  
pp. 99-103 ◽  
Author(s):  
Chia-Hui Ho
Keyword(s):  
Data Envelopment Analysis ◽  
Textile Industry ◽  
Market Competition ◽  
Operating Performance ◽  
Returns To Scale ◽  
Operating Efficiency ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Optimal Operating ◽  
Constant Returns To Scale

Abstract Operating performance could affect the survival and future development of a business that both businesses and business managers would devote to the enhancement of operating performance. Having developed for more than four decades, the consistent upstream, mid-stream and downstream system have been constructed in domestic textile industry. The output value of textiles in Taiwan has exceeded 480 billion NT dollars, which is not a sunset industry, as generally described. The impacts of high labour cost, environmental protection measures and changes of capital market as well as the competition of emerging countries, particularly Mainland China, have made textile industry in Taiwan face great market competition and pressure. Since textiles are regarded as one of the major products in Taiwan, the operating performance could affect the survival of the overall industry. In this case, operating performance survey of textile manufacturers in Taiwan during 2010–2012 is combined with Data Envelopment Analysis and Slack Variable Analysis to measure the total efficiency, pure technical efficiency and scale efficiency of top 12 textile manufacturers in Taiwan, tending to provide the reference of operating efficiency improvement for the manufacturers. The empirical results show that the overall efficiency in the 3 years appears 0.89 averagely. The relative efficiency (1) between two manufacturers, Far Eastern New Century and Ruentex Industries, achieves the optimal operating efficiency, whereas the remaining 10 are comparatively worse. Regarding the analysis of returns to scale, two textile manufacturers present constant returns to scale, with the optimal operating efficiency, whereas the remaining 10 show increasing returns to scale, revealing that expanding the scale could enhance the marginal return and further promote the efficiency.

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 Influence of Constant Returns to Scale and Variable Returns to Scale Data Envelopment Analysis Models in ICT Infrastructure Efficiency Utilization

2021 ◽  
pp. 158-181
Author(s):  
Yinka Oyerinde ◽  
Felix Bankole
Keyword(s):  
Data Envelopment Analysis ◽  
Human Development ◽  
Relative Efficiency ◽  
Returns To Scale ◽  
Data Envelopment ◽  
Information And Communication ◽  
Ict Infrastructure ◽  
Policy Direction ◽  
Constant Returns To Scale ◽  
Variable Returns To Scale

A lot of research has been done using Data Envelopment Analysis (DEA) to measure efficiency in Education. DEA has also been used in the field of Information and Communication Technology for Development (ICT4D) to investigate and measure the efficiency of Information and Communication Technology (ICT) investments on Human Development. Education is one of the major components of the Human Development Index (HDI) which affects the core of Human Development. This research investigates the relative efficiency of ICT Infrastructure Utilization on the educational component of the HDI in order to determine the viability of Learning Analytics using DEA for policy direction and decision making. A conceptual model taking the form of a Linear Equation was used and the Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) models of the Data Envelopment Analysis were employed to measure the relative efficiency of the components of ICT Infrastructure (Inputs) and the components of Education (Outputs). Results show a generally high relative efficiency of ICT Infrastructure utilization on Educational Attainment and Adult Literacy rates, a strong correlation between this Infrastructure and Literacy rates as well, provide an empirical support for the argument of increasing ICT infrastructure to provide an increase in Human Development, especially within the educational context. The research concludes that DEA as a methodology can be used for macroeconomic decision making and policy direction within developmental research.

scholarly journals Performance analysis in the presence of bounded, discrete and flexible measures_

2021 ◽  
Vol 31 (3) ◽  
Author(s):  
Sohrab Kordrostami ◽  
Monireh Jahani Sayyad Noveiri
Keyword(s):  
Relative Efficiency ◽  
Data Envelopment ◽  
Highway Maintenance ◽  
Real World Applications ◽  
The Status ◽  
Dea Models ◽  
Discrete Values ◽  
Flexible Measure ◽  
The Individual ◽  
University Performance

In conventional data envelopment analysis (DEA) models, the relative efficiency of decision making units (DMUs) is evaluated while all measures with certain input and/or output status are considered as continuous data without upper and/or lower bounds. However, there are occasions in real-world applications that the efficiency of firms must be assessed while bounded elements, discrete values, and flexible measures are present. For this purpose, the current study proposes DEA-based approaches to estimate the relative efficiency of DMUs where bounded factors, integer values, and flexible measures exist. To illustrate it, radial models based on two aspects, individual and aggregate, are introduced to measure the performance of entities and to handle the status of the flexible measure such that there are bounded components and discrete data. Applications of approaches proposed in the areas of quality management, highway maintenance patrols, and university performance measurement are given to clarify the issue and to show their practicability. It was found that the introduced procedure can determine practical projection points for bounded measures and integer values (from the individual DMU viewpoint) and can classify flexible measures along with evaluation of DMUs relative efficiency.

scholarly journals Productivity Benchmarking Using Analytic Network Process (ANP) and Data Envelopment Analysis (DEA)

2018 ◽  
Vol 2 (3) ◽  
pp. 27 ◽  
Author(s):  
Shanta Mazumder ◽  
Golam Kabir ◽  
M. Hasin ◽  
Syed Ali
Keyword(s):  
Data Envelopment Analysis ◽  
Returns To Scale ◽  
Analytic Network Process ◽  
Data Envelopment ◽  
Contributing Factors ◽  
Productivity Measurement ◽  
Apparel Manufacturing ◽  
Network Process ◽  
Dea Approach ◽  
Constant Returns To Scale

Measuring productivity is the systematic process for both inter- and intra-organizational comparisons. The productivity measurement can be used to control and facilitate decision-making in manufacturing as well as service organizations. This study’s objective was to develop a decision support framework by integrating an analytic network process (ANP) and data envelopment analysis (DEA) approach to tackling productivity measurement and benchmarking problems in a manufacturing environment. The ANP was used to capture the interdependency between the criteria taking into consideration the ambiguity and vagueness. The nonparametric DEA approach was utilized to determine the input-oriented constant returns to scale (CRS) efficiency of different value-adding production units and to benchmark them. The proposed framework was implemented to benchmark the productivity of an apparel manufacturing company. By applying the model, industrial managers can gain benefits by identifying the possible contributing factors that play an important role in increasing the productivity of manufacturing organizations.

scholarly journals Pengukuran Efisiensi Kinerja Sekolah Dasar lewat Model Data Envelopment Analysis

2017 ◽  
Vol 36 (2) ◽  
Author(s):  
Siti Fatimah ◽  
Umi Mahmudah
Keyword(s):  
Elementary Schools ◽  
Data Envelopment Analysis ◽  
Parametric Method ◽  
Returns To Scale ◽  
Scale Model ◽  
Data Envelopment ◽  
Public Elementary Schools ◽  
Crs Model ◽  
Using Data ◽  
Constant Returns To Scale

This study aims to measure the performance efficiency of elementary schools in Special Capital Region of Jakarta, especially Central Jakarta district in the period 2014/2015 by using data envelopment analysis (DEA) approach. DEA is a non-parametric method to measure efficiency of decision making units (DMUs). DEA compares several homogeneous DMUs based on a number of inputs to produce the expected outputs. This study uses descriptive method using DMU as many as 103 public elementary schools that are A-accredited with three inputs and four outputs. Data is analyzed using DEAP version 2.1 application by comparing CRS (Constant Returns to Scale) model and VRS (Variable Returns to Scale) model. Results show that: 1) in CRS model, there are 8 public elementary schools (7.77 percent) have efficient performances while in VRS model there are 14 public elementary schools (13.59 percent) have efficient performances; 2) VRS model is better than CRS model in measuring the efficiency performance of public elementary schools in Central Jakarta.

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