Evaluating the Efficiency of Heat and Power Systems by the Data Envelopment Analysis Method

The author of the article describes the theoretical assumptions of the DEA method used for measuring the productivity of airports described in the article ‘Research into the methods of analysing the productivity indicators of transport terminals’ (Jaržemskienė 2009). The essential insights presented in the above mentioned paper reveal that Data Envelopment Analysis (DEA) is a relatively new ‘data oriented’ approach to evaluating the performance of the so-called Decision Making Units (DMU) that convert multiple inputs into multiple outputs. The article focuses on the findings of the study carried out by the author in accordance with those assumptions. Research represents the Data Envelopment Analysis (DEA) method testing 15 selected airports situated in the Baltic Region, including Vilnius, Kaunas, Palanga, Riga, Tallinn, St. Petersburg, Helsinki, Turku, Stockholm, Malme, Copenhagen, Hamburg, Gdansk, Warsaw and Minsk. Airport productivity indicators are ranked considering importance and using the method of Delphi expert survey made of two rounds. The author presented the following indicators (expressed as ‘ratio’) as the major ones estimated by PAX/LAND, AIR/LAND, PAX/AIR, PAX/RW, PAX/RWA, GA and INTER experts. The succeeding indicators were introduced by PAX/TERMAREA, PAX/GATES, AIR/RW, AIR/RWA, AIR/TERMAREA, AIR/GATES, FR/LAND and FR/RW. 10 indicators were accepted as the most important and selected from the current set in the following sequence: AIR/LAND, AIR/RW, PAX/RW, PAX/LAND, AIR/RWA, PAX/AIR, PAX/RWA, AIR/TERMAREA, PAX/GATES and PAX/TERMAREA. AIR/LAND and AIR/RW were submitted as two main indicators. The acronyms are explained as follows: LAND – airport area, RWA – runway length, PAX – the number of passengers, AIR – the number of aircraft take-offs and landings, RW – the number of runways, GATES – the number of gates, FR – the amount of freight served, TERMAREA – the area of passenger terminal, GA – a general aviation market share of airport served aircraft by percentage, INTER – the percentage of international passengers considering all passengers served by airports. After two key productivity indicators were chosen conducting the expert survey, airport productivity was compared applying the DEA method.

The aim of this review was to present research work on the development of the Malmquist index (MI) in various aspects based on the Data Envelopment Analysis (DEA) method. The MI is one of the three methods of dynamic analysis and also the main one, as it is the most commonly used to measure productivity changes. However, over the years, from being a simple productivity index like the models within the DEA method, it has become a more complex and useful index as it has seen many developments and extensions. Modifications and developments of the MI have taken place in many directions. A review of MI studies from 1990 to 2025 shows that the authors considered various aspects of the MI: Alternative ways of decomposing, different ways of measuring, different types of efficiency, different nature of data and variables and network structure or combined dynamic analysis methods into a single framework. The direction of changes and modifications of the MI can be divided into three groups: Adapting DEA solutions within the MI, presenting new MI solutions without being inspired by the DEA method and combining two methods of dynamic analysis into one framework.

Data Envelopment Analysis (DEA) method is used to evaluate the efficiency of the bank branches. The present paper prioritized the indicators, using fuzzy questionnaires and by gathering the managers’ ideas, in order to obtain the most important evaluating indicators. After obtaining these indicators, Data Envelopment Analysis method was used to determine the performance bound interval, regarding the fact that the data are related to a certain period of time and their nature is imprecise and fuzzy. Then, Fuzzy Theory method and Fuzzy Data Envelopment Analysis method were employed to rank the branches, and finally the results were presented.

Entrepreneurship orientation (EO) approach provides universities with a plan and roadmap for getting over rising uncertainty and complexity. Inspiring universities to embrace transformation and innovation, risk taking and proactive policy for planning and executing development strategies for success in the dynamic competitive environment arise from entrepreneurship orientation spirit. To reach this status, the key factors of success in entrepreneurship should be taken into consideration and assessed in universities. Structural and entrepreneurial policies as key non-financial factors are two concepts that have received considerable attention over the past years. Measuring efficiency of universities by the mentioned factors and using mathematical programming like Data Envelopment Analysis (DEA) method is the aim of this study. Hence, this paper applied DEA method for measuring efficiency of 16 faculties and institutes in Ferdowsi University of Mashhad (FUM) in Iran. Descriptive-survey methodology was used and data were collected through questionnaire survey. The faculties and institutes were ranked based on structural policies and EO through DEA. Finally, the DEA method recommended the development roadmaps template for the 11 faculties and institutes which were inefficient.

This study focuses on the investigation of the influence of human capital factors on the tax collection efficiency. Literature review indicated that although the efficiency evaluation of tax collection has been discussed in previous study, the influence of human capital factors on the tax collection efficiency has seldom been addressed. Hence, the Data Envelopment Analysis (DEA) method has been employed to address this issue in this study. The innovation of this study lays on that the human capital factors have been taken into account in the efficiency evaluation of tax collection. To measure performance of the human capital factors in the tax collection, a case study in Wuhan city, China has been carried out. The DEA analysis results demonstrate that the tax collection efficiency increases with the increase of the number and higher education level of the tax officers and the higher education level of the tax officers still has a large room to improve. Then, relative improvement strategies have been proposed to strengthen tax collection efficiency. Hence, the findings of this study contribute to the body of knowledge in the efficiency evaluation of tax collection and its improvement measurement, which can be referred by other cities over the world.

This study aims to examine the determinants of the level of efficiency of banking performance as measured by the non-parametric Data Envelopment Analysis (DEA) method. The determinants of the level of efficiency in banking performance include bank size, Capital Adequacy Ratio, Loan to Deposit Ratio, Non Performing Loans and capitalization. The data in this study were obtained from the Indonesia Stock Exchange (BEI) from 2013 to 2017. The sampling technique used was purposive sampling. The sample used was 160 data from conventional commercial banks in Indonesia. Input variables for calculations using the DEA method are Third Party Funds and Total Assets. While the output variables used are credit distribution and operating income. The results showed that bank size and capitalization did not affect the level of banking efficiency. Meanwhile, Capital Adequacy Ratio and Non-Performing Loans negatifly affect the level of banking efficiency, while the Loan to Deposit Ratio has a positive effect on the level of banking efficiency.

Construction safety performance evaluation based on data envelopment analysis (DEA) from a hybrid perspective of cross-sectional and longitudinal

Selection of design alternatives for smart product service system: A rough-fuzzy data envelopment analysis approach

In order to make better assessment of intermodal container transportation, the authors have analyzed DEA (Data Envelopment Analysis) method in deciding the most favorable container line from Serbian ports to the near East ports. To allow for applications to a wide variety of activities, term organization unit (Decision Making Unit – DMU) refers to container line. DEA Method has been used to give the estimation of efficiency of operations in organization units. Organization units are container lines in which containers are loaded with mineral water in bottles. The main points in transportation net are factories of mineral water (Knjaz Milos Arandjelovac and Minaqua Novi Sad), Serbian ports ( the Port of Belgrade, the Port of Danube Pancevo and the Port of Prahovo) and nine Mediterranean ports in the near East area. Amount of mineral water that is dispatching from factories to Serbian ports is equal to amount that can be loaded to one container. Having containerized in Serbian ports mineral water is reloaded to the river–sea ships with unified dimensions, so the ships are continuing to travel to the near East ports. The near East ports are the ending points. Input variables in DEA problem are transportation costs and strategy resistance factor in function of container line. Time traveling of one container is in a function of container line and transportation capabilities of container lines are output variables. Transportation costs are counted on the base of integer programming method and Dijkstra algorithm.

The article presents the results of assessing the development efficiency of the tourism and recreation complex of Russia’s regions for the period from 2017 to 2021. This period includes both years of stability, characterized by predominance of positive trends in tourism development, and crisis and post-crisis years associated with the effects of the COVID-19 pandemic. Taking into account the complexity of the structure of the tourism and recreation system and the need to improve territorial planning and management, an urgent task is to introduce a comprehensive, aggregated efficiency indicator to be used for assessment of regional tourism. The efficiency coefficient (efficiency score) calculated by means of the DEA (Data Envelope Analysis) method is proposed as such an indicator. The study is novel in that it develops a methodology for assessing the efficiency of regional tourism based on DEA. The essence of this method is as follows: basing on the theory of duality, having data of input and output parameters, it is possible to calculate the value of the efficiency indicator for a set of N-objects (in our study – subjects, i.e., constituent entities of the Russian Federation). For the calculations, we used a model focused on the ‘input’ and taking into account the variable scale effect. In models of this type, each inefficient object is compared with efficient objects that have the structure (ratios) of indicator values closest to the structure of this inefficient object. This allows one to determine how much to change the parameters of the tourism and recreation complex in order to achieve efficiency equal to 1 (which is the maximum score). The model used seven ‘input’ indicators and six ‘output’ indicators. The analysis of the results was carried out in the context of the tourist macro-territories designated in the strategic documents for the development of tourism in the Russian Federation. The data obtained indicate a decrease in the efficiency of tourism development in the Russian Federation, with the exception of a number of regions and two tourist macro-territories. The results of the study can be used to adjust regional tourism development programs.

This study aims to analyze the technical efficiency of herbal product sales at PT X, Sleman branch, with a focus on determining the most efficient input and output variables. The research adopts a descriptive quantitative approach, utilizing the Data Envelopment Analysis (DEA) method to measure efficiency. Several input variables are assessed, including the number of active employees, basic salary, allowances, and bonuses, while the output variable considered is the total amount of herbal product sales. The results, obtained through efficiency measurement using the WinDEAP software, reveal that 50% of the Decision-Making Units (DMUs) are inefficient. Specifically, DMU 1, DMU 3, DMU 4, DMU 5, and DMU 6 fail to achieve optimal efficiency levels. To address this, recommendations for improvement are provided by referring to the efficient DMUs. These recommendations involve tailored input and output adjustments aimed at optimizing sales. Efficiency can be realized if each employee earns a salary of Rp 4,109,733.00 while achieving a minimum sales target of 146 herbal products. Following these recommendations, an assessment of employee performance is conducted. The findings indicate that 30% of marketing employees demonstrate efficient sales performance, whereas 70% still struggle to reach efficiency targets, necessitating further adjustments.

Data Envelopment Analysis (DEA) is a nonparametric approach to evaluating the relative efficiency of decision making units (DMUs) that use multiple inputs to produce multiple outputs. An assumption underlying DEA is that all the data assume the form of specific numerical values. In some applications, however, the data may be imprecise. For instance, some of the data may be known only within specified bounds, while other data may be known only in terms of ordinal relations. DEA with imprecise data or, more compactly, the Imprecise Data Envelopment Analysis (IDEA) method developed in this paper permits mixtures of imprecisely- and exactly-known data, which the IDEA models transform into ordinary linear programming forms. This is carried even further in the present paper to comprehend the now extensively employed Assurance Region (AR) concepts in which bounds are placed on the variables rather than the data. We refer to this approach as AR-IDEA, because it replaces conditions on the variables with transformations of the data and thus also aligns the developments we describe in this paper with what are known as cone-ratio envelopments in DEA. As a result, one unified approach, referred to as the AR-IDEA model, is achieved which includes not only imprecise data capabilities but also assurance region and cone-ratio envelopment concepts.

The method of data envelopment analysis (DEA) is a nonparametric statistical method, which is widely used in energy efficiency analysis of complex industrial process. However, there are many factors for affecting the energy efficiency, so the input and output indexes of the DEA is too much to get the low resolution of analysis results. Therefore, this paper proposes an energy efficiency analysis method based on the DEA cross model (DEACM) integrating principal component analysis (PCA) (PCA-DEACM). After using the PCA to reduce the dimensions of input and output indexes, the DEACM is used to further improve the effective resolution of decision-making units (DMUs). And the analysis results are more objective and reasonable. Finally, the proposed PCA-DEACM method is applied in energy efficiency analysis of PTA plants. By comparing with the classical DEA method and the PCA-DEA method, the validity and applicability of the PCA-DEACM is verified, which provides accurate analysis results for optimizing the operation of PTA plants.

Analyze the efficiency levels of Himbara banks after receiving Rp200 trillion in funds from the Ministry of Finance. Compare the efficiency levels between Himbara banks in the periods before and after the fund placement policy. Identify internal factors that influence the efficiency of government fund management at Himbara banks. This study uses a quantitative approach with the non-parametric Data Envelopment Analysis (DEA) method. This approach was chosen because it can measure the relative efficiency between decision-making units (DMUs) without requiring specific production function assumptions (Charnes, Cooper, & Rhodes, 1978). DEA allows researchers to evaluate the ability of Himbara banks to efficiently convert inputs (resources) into outputs (intermediation results). The analysis was conducted for the period 2024–2025, covering conditions before and after the placement of IDR 200 trillion in funds from the Ministry of Finance. To analyze factors influencing efficiency, the Tobit regression model was used. Data Envelopment Analysis (DEA) results show that the Rp200 trillion injection of funds was only effective in increasing efficiency at already productive banks (BRI, Mandiri, and BTN), while BNI still exhibited inefficiency due to its inability to convert additional funds into proportional credit growth and interest income. This indicates the need for improvements in credit distribution strategies, operational cost management, and risk management so that the injection of public funds truly has an optimal impact on efficiency and economic growth. Tobit Regression results indicate that bank efficiency in Indonesia tends to increase if banks have strong capital adequacy, good asset quality, and optimal intermediation capabilities. Conversely, increasing NPLs and excessive asset expansion can be factors that reduce efficiency. Therefore, future banking policy strategies need to focus on improving asset quality, cost efficiency, and implementing effective governance across all bank scales

The paper presents the results of simulation and evaluation of the efficiency of the system for the oil-contaminated wastes (OCW) recycling. The recycling system includes facilities for the OCW storing, technological installations for their processing and all necessary infrastructures that ensure the interconnection of the elements of the system under consideration. The system elements are evaluated using the basic CCR model and Super-efficiency model of the Data Envelopment Analysis (DEA) method. The algorithm has been developed to evaluate the efficiency of the OCW utilization system, which allows one to select the best tech-nologies and storage facilities for the OCW recycling, taking into account their relative estimations with respect to resource value and reuse potential, as well as logistic criterion evaluating trans-portation costs and ecological criterion evaluating environmental safety of recycling processes in the analyzed system.