scholarly journals THE PROPORTION OF SPORTS PUBLIC SERVICE FACILITIES BASED ON THE DEA MODEL IN COLLEGES AND UNIVERSITIES

2021 ◽  
Vol 27 (spe) ◽  
pp. 97-100
Author(s):  
Haonan Niu ◽  
Yu Zhang
Keyword(s):  
Decision Making ◽  
Public Service ◽  
Colleges And Universities ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Sports Facilities ◽  
Dea Model ◽  
Matching Efficiency ◽  
Decision Making Units ◽  
Selection Of

ABSTRACT In order to strengthen the physical education of college students, it is necessary to reasonably allocate university sports public service resources. In order to improve the allocation of university sports resources, this study constructs the Data Envelopment Analysis (DEA) model by analyzing the proportion of public sports service facilities in colleges and universities. Through the selection of input index and output index of sports public service facilities in colleges and universities, as well as selecting 20 colleges and universities as decision-making units, this paper constructs a DEA model, and studies the use of the DEA Tobit two-stage method to evaluate the matching efficiency of public sports service facilities in colleges and universities. The results show that the pure technical efficiency of sports public service facilities in colleges and universities is effective, and the scale efficiency of most colleges and universities is relatively high, and the proportion of sports facilities is relatively reasonable. However, there are still large problems in the proportion of public sports service facilities in colleges and universities, so it is necessary to adjust the proportion and scale of sports facilities allocation reasonably. This study verified the effectiveness of the DEA model in evaluating the proportion of public sports service facilities in colleges and universities, hoping to provide certain reference for improving the proportion of public sports service facilities in colleges and universities.

An optimistic-pessimistic DEA model based on game cross efficiency approach

2020 ◽  
Vol 54 (4) ◽  
pp. 1215-1230
Author(s):  
Mediha Örkcü ◽  
Volkan Soner Özsoy ◽  
H. Hasan Örkcü
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Equilibrium Solution ◽  
Data Envelopment ◽  
Optimal Weights ◽  
Discriminating Power ◽  
Proposed Model ◽  
Essential Problem ◽  
Decision Making Units ◽  
Selection Of

The ranking of the decision making units (DMUs) is an essential problem in data envelopment analysis (DEA). Numerous approaches have been proposed for fully ranking of units. Majority of these methods consider DMUs with optimistic approach, whereas their weaknesses are ignored. In this study, for fully ranking of the units, a modified optimistic–pessimistic approach, which is based on game cross efficiency idea is proposed. The proposed game like iterative optimistic-pessimistic DEA procedure calculates the efficiency scores according to weaknesses and strengths of units and is based on non-cooperative game. This study extends the optimistic-pessimistic DEA approach to obtain robust rank values for DMUs. The proposed approach yields Nash equilibrium solution, thus overcomes the problem of non-uniqueness of the DEA optimal weights that can possibly reduce the usefulness of cross efficiency. Finally, in order to verify the validity of the proposed model and to show the practicability of algorithm, we apply a real-world example for selection of industrial R&D projects. The proposed model can increase the discriminating power of DMUs and can fully rank the DMUs.

scholarly journals Allocation of weights using simultaneous optimization of inputs and outputs contribution in cross-efficiency evaluation of DEA

2018 ◽  
Vol 28 (4) ◽  
pp. 521-538
Author(s):  
Seyed Nasseri ◽  
Hamid Kiaei
Keyword(s):  
Decision Making ◽  
Simultaneous Optimization ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Optimal Solutions ◽  
Specific Criterion ◽  
Use Of Resources ◽  
Multiple Weights ◽  
Decision Making Units ◽  
Selection Of

Cross-efficiency evaluation, an extension of the data envelopment analysis (DEA), has found an appropriate function in ranking decision making units (DMU). However, DEA suffers from a potential aw, that is, the existence of multiple optimal solutions. Different methods have been proposed to obtain a unique solution (based on a specific criterion). In this paper, we refer to Wang's method for ranking DMUs but argue that his way of selecting the weights is not the appropriate one. Namely, in the cross-efficiency evaluation of DMUs, we always search for the weights which use minimum resources to increase the production. Therefore, we suggest that the selection of weights among the multiple weights should be determined by decreasing the contribution of inputs in the use of resources, and increasing the contribution of outputs in the production, which should overtly prevent the selection of zero solutions to the extent possible. To this end, some examples are given to illustrate differences and advantages of our method compared to those usually used.

Penilaian Terhadap Kecekapan Relatif Perpustakaan Di Institusi Pengajian Tinggi Awam (IPTA) Malaysia

2012 ◽  
Author(s):  
Mohamad Shukri Abdul Hamid ◽  
Wan Rosmanira Ismail
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Public University ◽  
Pareto Optimum ◽  
Pareto Optimal ◽  
Data Envelopment ◽  
University Libraries ◽  
Dea Model ◽  
Dual Analysis ◽  
Decision Making Units

Kajian ini membincangkan penggunaan kaedah analisis penyampulan data (APD) dalam mengukur kecekapan relatif perkhidmatan perpustakaan di institusi pengajian tinggi awam (IPTA) di Malaysia. Kaedah APD adalah salah satu teknik yang sesuai digunakan untuk menilai kecekapan relatif satu set unit pembuatan keputusan (UPK) yang homogen. Konsep asas APD ialah mengenal pasti UPK yang cekap berbanding dengan UPK yang lain. UPK yang cekap ini dikenali sebagai Pareto optimum dan ianya dijadikan asas untuk perbandingan dengan UPK yang tidak cekap. Dalam kajian ini perpustakaan yang didapati tidak cekap akan dijalankan analisis kedualan supaya ketidakcekapan dapat diterjemahkan kepada bentuk input dan output. Hasil daripada analisis kedualan ini pihak pengurusan dapat melakukan penambahbaikan supaya perpustakaan yang didapati tidak cekap akan mencapai kecekapan melalui pengurangan dalam input dan pertambahan dalam output. Input dan output yang sesuai diambil kira dalam penentuan kecekapan setiap UPK ditentukan menggunakan ujian isotonisiti. Kajian ini dilakukan ke atas 14 buah perpustakaan IPTA di Malaysia dan hasil kajian menunjukkan 8 buah perpustakaan adalah cekap manakala 6 buah perpustakaan tidak cekap. Kata kunci: Analisis penyampulan data; unit pembuat keputusan; kecekapan relatif; set rujukan; pareto optimum This research discusses Data Envelopment Analysis (DEA) model to evaluate the relative performance of public university libraries in Malaysia. DEA model is a suitable method to evaluate relative performances of a set of homogeneous decision–making units (DMUs). The basic concept of DEA is to identify the efficient decision–making units among all DMUs. This efficient DMU is called a Pareto optimal unit and is considered as the standard for comparison of all inefficient DMUs. In this study, the dual analysis will be carried out for the inefficient libraries in order to interpret the inefficiency in terms of the inputs and outputs. The outcomes of this dual analysis will give the management the improvements that need be done to the inefficient libraries, in terms of reducing the inputs and increasing the outputs. Suitable inputs and outputs for evaluating the performance of DMUs are determined using the isotonicity test. The study is carried out on 14 public university libraries and the results showed that 8 libraries are efficient while 6 are inefficient. Key words: Data envelopment analysis; decision-making unit; relative efficiency; reference set; pareto optimal

scholarly journals Analyzing the efficiency bank branches via novel weighted stochastic imprecise data envelopment analysis

2021 ◽  
Author(s):  
Nezir Aydın ◽  
Gökhan Yurdakul
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Sensitivity Analyses ◽  
Data Envelopment ◽  
Imprecise Data ◽  
Data Set ◽  
Dea Model ◽  
Imprecise Data Envelopment Analysis ◽  
Efficiency And Productivity ◽  
Decision Making Units

As of 21 th century, the terms of efficiency and productivity have become notions which dwells on both business and academic world more frequently compared to past. It is known that it is hard to increase the efficiency and productivity of both production and service systems. In this study, the efficiency analysis of the branches of a bank was conducted. Furthermore, a Weighted Stochastic Imprecise Data Envelopment Analysis (WSIDEA), which is a new approach developed based on Data Envelopment Analysis (DEA), was proposed. Efficiency levels and results of decision-making units were examined according to the proposed new method. Additionally, six different DEA model results are obtained. The results of the six different DEA model and the proposed "WSIDEA" model were compared in terms of efficiency level of decision-making units, and the differences between them were examined. Sensitivity of the inefficient units were also examined. On the other hand, unrealistic efficiency levels created by traditional methods for branches were also analyzed. Apart from all these sensitivity analyses, the sensitivity of the data set used in the analysis is scrutinized.

scholarly journals An Extension of Cross Redundancy of Interval Scale Outputs and Inputs in DEA

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Farhad Hosseinzadeh-Lotfi ◽  
Gholam-Reza Jahanshahloo ◽  
Mansour Mohammadpour
Keyword(s):  
Decision Making ◽  
Linear Combination ◽  
Data Envelopment ◽  
Ratio Scale ◽  
Interval Scale ◽  
Output Variable ◽  
Input And Output ◽  
Dea Models ◽  
Decision Making Units ◽  
Selection Of

It is well known that data envelopment analysis (DEA) models are sensitive to selection of input and output variables. As the number of variables increases, the ability to discriminate between the decision making units (DMUs) decreases. Thus, to preserve the discriminatory power of a DEA model, the number of inputs and outputs should be kept at a reasonable level. There are many cases in which an interval scale output in the sample is derived from the subtraction of nonnegative linear combination of ratio scale outputs and nonnegative linear combination of ratio scale inputs. There are also cases in which an interval scale input is derived from the subtraction of nonnegative linear combination of ratio scale inputs and nonnegative linear combination of ratio scale outputs. Lee and Choi (2010) called such interval scale output and input a cross redundancy. They proved that the addition or deletion of a cross-redundant output variable does not affect the efficiency estimates yielded by the CCR or BCC models. In this paper, we present an extension of cross redundancy of interval scale outputs and inputs in DEA models. We prove that the addition or deletion of a cross-redundant output and input variable does not affect the efficiency estimates yielded by the CCR or BCC models.

The Efficiency Performance of the Turkish Ceramic Sector in Terms of Revenue and Export

2015 ◽  
pp. 559-579
Author(s):  
Füsun Yenilmez
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Tile Production ◽  
Dea Model ◽  
Industrial Companies ◽  
The World ◽  
Efficiency Performance ◽  
Decision Making Units

Turkey is one of the leading countries in the world in the field of tile production and exportation. The ceramic sector constitutes the main focus and scope of this chapter, which makes some suggestions and recommendations to the industrial companies which were ranked among the 1,000 largest companies in 2011 based on their performance in revenue and exportation. Data Envelopment Analysis (DEA) is used in the measurement of the ceramic companies' performances. Three inputs (net actives, number of workers, and equity) and one output (revenue) are used in the first analysis where nine companies are taken as decision-making units. This chapter shows that Eczacibasi and Vitra were efficient in revenue making in 2011. Likewise, three inputs and one output (export) are used in the second analysis where nine companies are taken as decision-making units. According to DEA model findings, only Vitra was efficient in exporting in 2011.

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.

Forecast of the efficiency confidence interval of decision-making units in data envelopment analysis

2020 ◽  
Vol 33 (02) ◽  
pp. 431-445
Author(s):  
Azarnoosh Kafi ◽  
Behrouz Daneshian ◽  
Mohsen Rostamy-Malkhalifeh ◽  
Mohsen Rostamy-Malkhalifeh
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Confidence Interval ◽  
Data Envelopment ◽  
Data Set ◽  
Interval Model ◽  
Time Period ◽  
Proposed Model ◽  
The Future ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is a well-known method for calculating the efficiency of Decision-Making Units (DMUs) based on their inputs and outputs. When the data is known and in the form of an interval in a given time period, this method can calculate the efficiency interval. Unfortunately, DEA is not capable of forecasting and estimating the efficiency confidence interval of the units in the future. This article, proposes a efficiency forecasting algorithm along with 95% confidence interval to generate interval data set for the next time period. What’s more, the manager’s opinion inserts and plays its role in the proposed forecasting model. Equipped with forecasted data set and with respect to data set from previous periods, the efficiency for the future period can be forecasted. This is done by proposing a proposed model and solving it by the confidence interval method. The proposed method is then implemented on the data of an automotive industry and, it is compared with the Monte Carlo simulation methods and the interval model. Using the results, it is shown that the proposed method works better to forecast the efficiency confidence interval. Finally, the efficiency and confidence interval of 95% is calculated for the upcoming period using the proposed model.

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