Charnes , Cooper And Rhodes Research Articles

Measuring the efficiency of four star hotels in Makarska-Croatia using data envelopment analysis

This study explores the scale efficiency of four star hotels in a small tourist destination in Croatia. The number of overnight stays and the increase in hotel beds are two indicators of the development of a tourist destination. Among the accommodation facilities, hotels play a significant role in the development of a tourist destination, but they are increasingly facing a labor force crisis. Data envelopment analysis is used to rank hotels by efficiency coefficient. The aim of the paper is to investigate the efficiency of the hotel by taking certain inputs and outputs, which are explained in detail in the paper. The paper uses the CCR (Charnes, Cooper, and Rhodes) and BCC (Banker, Charnes, and Cooper) models to calculate hotel scale efficiency and also presents an overview of previous research around the world.

Determining Efficiency of 15 OECD Countries Coping with Covid-19 using Data Envelopment Analysis after 2 Years of Pandemic

Covid-19, emerged in Wuhan, a city of China, at the date of December 2019, it spread to the entire world soon and then by the World Health Organization (WHO) accepted as a pandemic. Today, still the coping with Covid-19 is the one of the significant topics of countries. In this study, the relative efficiencies of 15 OECD countries for coping with the pandemic is analyzed with Data Envelopment Analysis (DEA). Current health expenditure (% of GDP), Nurses and midwives (per 1 thousand people), Hospital beds (per 1 thousand people), Physicians (per 1 thousand people) variables used as inputs; Number of Total Cases (per 1 million people), Number of Deaths (per 1 million people) and Number of Tests (per 1 million people) variables used as outputs. Output oriented Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) DEA models are used, scale efficiency values are determined and potential improvement suggestions are given for inefficient countries. Austria, Mexico, South Korea, Spain and Türkiye are found to be both relative technical, total and scale efficient. It is also concluded that inefficient countries must use their input variables efficiently and number of Covid-19 tests is important for a country’s efficiency against Covid-19.

A new parallel framework algorithm for solving large-scale DEA models

With the rapid development of social informatization and platform economy, evaluating high-frequency and large-scale data has attracted widespread attention. One relevant and current research topic deals with finding high-quality decision-making units (DMUs) and scoring each DMU using a shorter computing time. The Framework method (Khezrimotlagh et al., 2019) has the fastest computing time so far. We propose a new parallel framework algorithm in this study. This algorithm effectively reduces the calculation time for finding all efficient DMUs and evaluate all DMUs in the BCC (Banker, Charnes and Cooper) model, the CCR (Charnes, Cooper and Rhodes) model, FG (Färe and Grosskopf) model and ST (Seiford and Thrall) model. The proposed algorithm has a reasonable selection method and optimal numbers of initial hull which can decrease the running time effectively. The extensive experimental results presented in this study show that when the input and output data are generated by uniform approach or normal approach, the density will increase with the increase of input and output dimensions, and show a growth trend like that of the S curve, which is similar to the distribution function of the Gamma distribution. We also tested our method using a real-life dataset. Compared with the previous methods, our method is shown to be more suitable for the input and output data generated by normal approach and decreases the calculation time by at least 12.52%. Furthermore, it has strong advantages in decreasing calculation time of solving the CCR model irrespective of whether the input data and output data are uniformly distributed or normally distributed.

Measuring performance of farmer producer organisations using data envelopment analysis

PurposeFarmers producer organisations (FPOs) play the most crucial role in the agriculture supply chain system, aiming to redress the balance between farming and marketing activities of agricultural produce. The purpose of this study is to assess the performance of FPOs using data envelopment analysis (usually referred to as DEA) on 34 FPO units selected from the state of Rajasthan.Design/methodology/approachOne of the most commonly used techniques to examine business performance is the application of DEA. The application of DEA requires the selection of inputs and outputs. This study takes three inputs and three outputs based on the insights drawn from the field survey. While the input variables consist of total assets, paid-up capital and the number of economic activities, the three output variables are turnover, net profit and number of members benefitted. Broadly, these variables encapsulate the operational performance of the business units.FindingsThis study’s findings reveal that the estimated relative efficiency score of the input-oriented CCR (Charnes, Cooper, and Rhodes) model ranges from 0.06 to 1. Interestingly, only one FPO has reported a relative efficiency (RE) score of one, whereas the remaining FPOs fall below the efficiency frontier. However, 15 FPOs report an RE score of one in the output-oriented CCR approach. Considering the estimates obtained in the input- and output-oriented BCC (Banker, Charnes and Cooper) models, this study found that about 20% of the FPOs report an efficiency score greater than 0.80. Moreover, three FPOs are on the frontier line. An examination of the scale efficiency score in the input-oriented model, 45% of the FPOs have an efficiency score greater than 0.80, whereas almost all FPOs achieve a scale efficiency score greater than 0.80 in the output-oriented model. Overall, the results imply that the FPOs should place greater emphasis on the efficient utilisation of the inputs to enhance the overall business performance and productivity.Research limitations/implicationsThe findings of this study provide vital insights into the specific inputs and outputs that determine the performance efficiency of FPOs and identify the potential areas for improving the existing inefficient FPOs.Originality/valueThis study contributes to the repository of the existing empirical studies in three distinct ways. First, the authors hardly found any previous studies that quantitatively assess the business performance of FPOs using the DEA technique. Second, the effort to identify the slacks associated with each input and output variable in input- and output-oriented models gives insights on improvable areas for inefficient FPOs. Third, the authors attempt to demystify the empirical obfuscations by highlighting the major challenges FPOs face in the state of Rajasthan.

Data Envelopment Analysis for the Efficiency of Higher Education Departments at Sepuluh Nopember Institute of Technology, Indonesia

<p style="text-align: justify;">The quality of higher education is vital for a country’s future, not only in terms of transferring knowledge to younger generation but also for supporting economic development. This paper applies data envelopment analysis (DEA) to evaluate the relative efficiency of 38 academic departments at Sepuluh Nopember Institute of Technology, Surabaya, Indonesia. The input factors are the number of lecturers, the number of staff and budget allocations, whereas the output is the performance achievement level. The empirical analysis incorporates two traditional DEA models: the Charnes, Cooper and Rhodes (CCR) and the Banker, Charnes and Cooper (BCC) models with input orientation. The results indicate that the CCR model considers five departments efficient while the BCC model considers ten departments efficient, five of which are those considered efficient by the CCR model. It may seem counterintuitive that a department with an output performance achievement below 100% is deemed efficient, and vice versa. However, the underlying principle of efficiency under input-oriented DEA model is resource utilization. Finally, we provide recommendations for the departments with low efficiency scores to improve their performance.</p>

Municipal water supplies efficiencies in Gaza Strip: a data envelopment analysis approach

This paper reports the findings of a research carried out to evaluate the efficiency of municipal water supply systems using Data Envelopment Analysis (DEA). Input and output variables needed for DEA were identified using literature review and questionnaires. Input variables considered in this paper include Number of Connections (NC), Length of Water Network (LWN), Number of Employees (NE) and Maintenance and Operation Costs (MOC), while Total Revenues (TR) and Number of People Served (NPS) were used as output variables. Values of these variables were obtained for the existing 25 municipalities for the years of 2015, 2016 and 2017 and the averages of these variables were calculated and used in the model. The paper uses Charnes, Cooper and Rhodes (CCR) model and Banker, Chames and Cooper (BCC) model. Results indicate that length of water network and maintenance and operation costs were the major sources of inefficiencies.

Determination of Digital Density Efficiency by Data Envelopment Analysis: EU Member States

This study aims to determine the digital intensity levels, which are an indicator of the digital transformation performance of the countries. With this scope, the digital intensity efficiency levels of the European Union member countries were calculated by data envelopment analysis (DEA). The input-oriented Charnes Cooper and Rhodes (CCR-O) Model has been used to determine the digital intensity of the countries. The input criteria of the DEA model were obtained from the digital skills data in The Digital Economy and Society Index-2021 (DESI), and the output criteria were obtained from the digital intensity data of the same index. Digital content creation communication and collaboration, online information and communication, information and data literacy, problem-solving, and safety skills are input criteria for digital skills. Very low, low, high, and very high data were used as output criteria for digital intensity. According to the findings, Hungary, Spain, Slovakia, and Ireland are not at the full efficiency level. It is also stated in the study that input variables should be directed to reach the full efficiency level of the countries that are not at the full efficiency level. Accordingly, it was observed that Hungary, Spain, Ireland and Slovakia are not at the full productivity level. it can be said that these countries have digital skills but their digital intensity is not high. In this respect, it can be concluded that they do not make use of these skills sufficiently.

Health System Efficiency of OECD Countries with Data Envelopment Analysis

Purpose: This study is aimed at measuring the efficiency of 37 OECD countries for 2020 using the Data Envelopment Analysis (DEA) method. Besides, it is aimed at ranking the efficient decision making units by using the super-efficiency DEA model. Design/methodology/approach: In the study, analyses were carried out with input-oriented Charnes, Cooper and Rhodes (CCR), input-oriented Banker, Charnes and Cooper (BCC) models and super-efficiency models of these models by using 4 inputs and 3 outputs. Findings: As a result of the analysis, 14 countries in the CCR model and 20 countries in the BCC model were efficient. According to the results of the super-efficiency models, the efficient countries were ranked. Research limitations/implications: The limitations of the study are the analyses are based on input-oriented DEA models and the research was conducted in OECD countries. Originality/value: Performance evaluation of health systems has gained importance in recent years. Many countries are making efforts to improve their health systems. Due to epidemics such as COVID-19, OECD countries, like many countries around the world, have increased the share of health expenditures in GDP. Because of this situation, the evaluation of the performance of OECD countries in the field of health has emerged as a very important research topic.

Data envelopment analysis cross-efficiency of primary health care in Lagos metropolis, Nigeria

The goal of this study is to assess primary healthcare's efficiency and ranking throughout the Lagos metropolis, Nigeria. These centers are responsible for delivering primary healthcare services. Primary and secondary data collection methods were used to collect 2019 data from fifteen primary healthcare facilities (PHCs) on four input variables (beds, medical and non-medical staff, outpatients and inpatients), and three output values (antenatal, deliveries, and quality of care), as well as from the Lagos State Primary Health Care Board (LSPHCB). Three models were used to assess the effectiveness of PHC in the Lagos metropolis: CCR (Charnes, Cooper, and Rhodes) Data Envelopment Analysis (DEA), Cross-efficiency (Aggressive and Benevolent) model, and the Maverick index. An R software code was written to solve the models. The results indicate that nine PHCs were efficient and six were inefficient, with cross-efficiency ratings ranging from 0.2491 to 0.6815 for aggressive PHCs and 0.3977 to 0.9206 for benevolent PHCs, respectively. Aina-ajobo, Ogundimu, and Odunmbaku obtained the highest rank in the aggressive model with cross-efficiency 0.6815, 0.6269, 0.5321; and Ogundimu, Aina-ajobo, and Odunmbaku obtained the highest rank in the benevolent model with cross-efficiency 0.9206, 0.9190, 0.8357; while Dopemu, powerline, and Shoretire obtained the lowest rank in the benevolent model Aina-ajobo and Ogundimu achieved the best outcomes across all models. Additionally, the Maverick index indicates that all the PHC are maverick in the aggressive with nine false positive efficiency and eleven maverick in the benevolent, with six false positive efficiency. From the analysis of the three models, none of the PHC in Lagos metropolis is operating efficiently. The cross-efficiency analysis fully discriminates between all PHCs; the scores are unique and give comparable rankings; and the overall performance objective is met.

Data Envelopment Analysis and Bootstrap Approaches for Efficiency Measure of the Autonomous Port of Dakar

In this article, we measure the e_ciency of the Autonomous Port of Dakar (APD) and identify the causes of inefficiency for the year 2021. Measuring port e_ciency is an important factor in strengthening its competitiveness and stimulating national development. In the literature, the most widely used methods for measuring e_ciency in the port sector are parametric and non-parametric methods. Our objective is to apply Data Envelopment Analysis (DEA) with two models, namely: the Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) to determine the efficiency scores and the bootstrap approach the Simar and Wilson in 2007 to correct the errors and determine the confidence intervals. Numerical simulations are performed in the two distinct zones separated by a Fishing Port (FP), Naval Repair Workshops (NRW) and the Military Zone (MZ), while the others zones are the Decision-Making Units (DMUs), detailed in table 2. The results show that APD obtains six (6) e_ective DMUs with CCR model (average score of 0.858) and ten (10) effiective DMUs with BCC model (average score of 0.951). The average scale efficiency is 0.897. With the bootstrap approach, we obtain an average bias-corrected of 0.700 for CCR model (with confidence intervals of [0.324; 0.1291]) and 0.870 for BCC model (with confidence intervals of [0.620; 1.197]). These results will allow the decision makers of the Dakar port authority to improve its performance and competitiveness at the national and international levels.

Evaluating the Operational Efficiency and Quality of Tertiary Hospitals in Taiwan: The Application of the EBITDA Indicator to the DEA Method and TOBIT Regression.

This study estimates the efficiency of 19 tertiary hospitals in Taiwan using a two-stage analysis of Data Envelopment Analysis (DEA) and TOBIT regression. It is a retrospective panel-data study and includes all the tertiary hospitals in Taiwan. The data were sourced from open information hospitals legally required to disclose to the National Health Insurance (NHI) Administration, Ministry of Health and Welfare. The variables, including five inputs (total hospital beds, total physicians, gross equipment, fixed assets net value, the rate of emergency transfer in-patient stay over 48 h) and six outputs (surplus or deficit of appropriation, length of stay, the total relative value units [RVUs] for outpatient services, total RVUs for inpatient services, self-pay income, modified EBITDA) were adopted into the Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) model. In the CCR model, the technical efficiency (TE) from 2015–2018 increases annually, and the average efficiency of all tertiary hospitals is 96.0%. In the BCC model, the highest pure technical efficiency (PTE) was in 2018 and the average efficiency of all medical centers is 99.1%. The average scale efficiency of all medical centers was 96.8% in the BBC model, meaning investment can be reduced by 3.2% and the current production level can be maintained with a fixed return to scale. Correlation coefficient analysis shows that all variables are correlated positively; the highest was the number of beds and the number of days in hospital (r = 0.988). The results show that TE in the CCR model was similar to PTE in the BCC model in four years. The difference analysis shows that more hospitals must improve regarding surplus or deficit of appropriation, modified EBITDA, and self-pay income. TOBIT regression reveals that the higher the bed-occupancy rate and turnover rate of fixed assets, the higher the TE; and the higher number of hospital beds per 100,000 people and turnover rate of fixed assets, the higher the PTE. DEA and TOBIT regression are used to analyze the other factors that affect medical center efficiency, and different categories of hospitals are chosen to assess whether different years or different types of medical centers affect operational performance. This study provides reference values for the improvable directions of relevant large hospitals’ inefficiency decision-making units through reference group analysis and slack variable analysis.

Eco-efficiency Assessment of Chinese Petrochemical Enterprises: A Data Envelopment Analysis Approach

This study uses the Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BBC) models of Data Envelopment Analysis (DEA) to evaluate the relative eco-efficiency and operating efficiency of the ten petrochemical enterprises. A random sample of ten petrochemical enterprises were selected from the Northeastern area of China. The data collected from the ten petrochemical enterprises were run on the DEA models and the evaluated results were input for the difference analysis for the scale efficiency and technical efficiency. Then the estimates the petrochemical enterprise's operating efficiency and ecological efficiency along with the influencing factors were run by regression analysis in order to verify the evaluation model and the rationality of influencing factors. After the projection analysis of the DEA, the firms that did not reach the values of effectiveness were identified and provided the suggestions for the targeted improvement values of eco-efficiency. Thus, this application procedure can be viewed as an example of the application development of the new DEA model and provides the reference for related industry making sustainable development strategy.

Modelling NPLs and identifying convergence phenomenon of banks: a case of pre-during and immediate after the crisis years in India

PurposeComparing conventional data envelopment analysis (DEA) model with contemporary Seiford and Zhu model, this study aims to evaluate the technical efficiency (TE) of Indian banks from 1998/99 to 2016/17 in the presence of non-performing loans (NPLs).Design/methodology/approachTo examine TE, this study has considered a novel approach, i.e. linear monotone decreasing transformation as suggested by Seiford and Zhu (2002), which treats undesirable output as a desirable output in the framework of Charnes, Cooper and Rhodes (CCR)-based output-oriented DEA approach. In particular, to remove the biasness from the estimated efficiency scores, Simar Wilson (1998, Algorithm #1) has been applied, which is perhaps the first attempt in this kind of literature till now. This study further tries to investigate the notion of sigma and unconditional β-convergence in TE using two-step system generalized method of moments model in dynamic panel framework.FindingsTreatment of NPLs using conventional DEA model misinterprets the TE scores, while a true picture emerges when the NPLs are correctly accounted as an undesirable output in banks’ loans production process. Efficiency has declined during the crisis years, but it recovered immediately after the crisis years in India. However, a sudden and steep deterioration in efficiency scores has been seen from 2013 till the most recent study period. Public sector banks and old private banks have reported higher average efficiency scores than new private banks (NPBs) and foreign banks (FBs) in India. However, FBs are the only commercial banks that maintained their efficiency levels during crisis years in India. This study also saw the persistence and presence of σ-convergence phenomena in TE for Indian banks, reflecting the ability to reach up to “Catch-up” phenomenon owing to the lower dispersion and persistence of convergence in TE by the Indian banks.Practical implicationsThe actual efficiency score can only be estimated when the NPL will be considered as an undesirable output rather than a desirable output when designing the loan production process of banks. Although the ownership clusters of all commercial banks in India need to formulate stricter policies to increase the level of assets quality and efficiency, but, NPBs need to pay some more efforts in this direction. This study’s outcome has the potential to provide useful information for regulators and policymakers, which suggests that in which direction or in which clusters improvement are needed to raise the level of asset quality and technical efficiency in the coming years.Originality/valueFor a long time, there has been the existence of trade-offs between researchers, like which is the best model for accounting for NPLs? Traditional or contemporary? Thus, our study aims to add knowledge to the limited stock of NPL modelling in the efficiency literature. Dynamic convergence in TE scores in Indian banks has yet not to be tested, which is another novelty of the study.

Determining the Effectiveness of Sustainable Production Activities in Fishing Sector by Data Envelopment Analysis

Data envelopment analysis is used when it is difficult to measure the relative effectiveness of organizational decision-making units due to a large number of similar inputs and outputs. Firstly, data envelopment analysis was discussed in this study. In this context, relative activities of 10 companies have been operating in fisheries sector for last nine years (2009-2017) which entered the Fortune 500 in Turkey and have sufficient data were measured using financial inputs and outputs. Relative effectiveness scores were obtained by examining the financial inputs and outputs of the companies that are in the Fortune 500 and operating in fisheries sector. In the second part of study, to reveal the technical efficiency and the ineffective ones resulting from the scale, DEA models including the input-oriented CCR (Charnes, Cooper, and Rhodes) and BCC (Banker, Charnes, and Cooper) model were established and the above mentioned procedures were repeated. Finally, it was investigated whether an investment system based on DEA could be established or not. DEA Frontier and DEA Solver software, one of the special software of DEA, was used for the solution of models using in data envelopment analysis. As a result of the study, the average efficacy percentage was found to be 88% for CCR and 93% for BCC. For the data obtained during 2009-2017, six companies were found to be active according to the CCR model, while seven companies were found to be effective according to the BCC model. Also, the targets were determined to activate the inactive companies.

VCS and CVS: New combined parametric and non-parametric operation research models

The constancy of efficacy derived from parametric and non-parametric is not significant, this paper provides a scientific assessment and proposes two novel combined parametric and non-parametric operation research models, which will be a new experiment in literature perception. A scientific assessment of banks based on two novel optimizations VRS-CRS-SFA (VCS) and CRS-VRS-SFA (CVS) as the combination of the efficiency measurement method of CCR(Charnes, Cooper and Rhodes model) or CRS(Constant Return to Scale), BCC(Banker, Charnes, and Cooper model) or VRS(Variable Return to Scale) in Data Envelopment Analysis (DEA), as well as Stochastic Frontier Approach (SFA) for 65 banks during Feb to July 2020 are introduced. For analyzing the performance of the parametric and non-parametric approaches, we have considered the linear regression and Unreplicated Linear Functional Relationship (ULFR). Finally, the superior bank and the best performance model are introduced. For more clarification, three different approaches, which are production, intermediation, and profit/revenue in financial institutions, are considered. Among the proposed techniques, the two novel recommended VCS and CVS compared with BCC-CCR, CCR-BCC, and SFA models, in all of the three suggested approaches have a more positive correlation with profit risk and show the higher coefficient of determination values.

A hybrid framework for evaluating the performance of port container terminal operations

This work intends to integrate artificial neural network (ANN) and data envelopment analysis (DEA) in a single framework to evaluate the performance of operations in the container terminal. The proposed framework is based on three steps. In the first step, a proposed identify the performance measures objectives and the indicators affecting the system. In the second step, the efficiency scores of the system are computed by using the Charnes Cooper and Rhodes (CCR) model (oriented inputs). In the last step, the Moth Search Algorithm (MSA) is employed as a new method for training the Feedforward Neural Network (FNN) to determine the efficiency scores. To demonstrate the efficacy of the proposed framework, two container terminals of Tangier and Casablanca are adopted to evaluate the performance.

Supply Chain Performance Analysis With Data Envelopment Analysis in TBBM PT. Pertamina Boyolali

PT. PERTAMINA TBBM Boyolali is a supplier of fuel oil in the form of premium, pertamax and diesel. The company should distribute fuel products using tank cars with operating areas covering Boyolali, Surakarta, Karanganyar, Sukoharjo, Sragen, Klaten, Wonogiri, Ngawi, Pacitan, Magetan, Ungaran, Semarang, Salatiga, Purwodadi, and Blora. An efficient supply chain is needed to provide the right products and services, with the specifications needed, in the right place and to the right customers. This article aims to find out and analyze the efficiency that occurs at operational TBBM Pertamina Boyolali and to find out the variables that most influence supply chain performance to be analyzed and improved so that improvements in supply chain performance can be more focus and optimal. The Decision-Making Units (DMUs) stated in this article are the period of supply chain activities from January 2018 to November 2018 at TBBM Pertamina Boyolali, the number of DMU is 11 DMUs. Supply Chain Operation Reference (SCOR) method with responsiveness, flexibility, and reliability attributes is used to measure Supply chain performance. To analyze supply chains efficiency, this article applies input-oriented DEA CCR (Charnes, Cooper, and Rhodes) model which considers linear relationships in its input and output. Data Envelopment Analysis (DEA) is a strong tool for measuring the efficiency of the DMUs and does not need to normalize indicator values that have different dimensions. The software used for processing data in this article is Banxia Frontier Analyst 4.2. This article provides suggestions for improving cash-to-cash cycle time, lead time, and flexibility for more efficient fuel distribution.

Economic and energy efficiency of agriculture

Article concerns economic and energy efficiency of agriculture in European Union countries. The study period concerned 2016. For analysis and presentation of materials, descriptive, tabular and graphic methods and the Data Envelopment Analysis (DEA) method – CCR (Charnes, Cooper and Rhodes) model focused on input-oriented minimisation were used. An assessment of the socio-economic development of the EU countries was made using the following measures: Human Development Index (HDI) and Gross Domestic Product (GDP) per capita (USD per inhabitant). Modern agriculture depends on industrial energy sources and as the socio-economic development changes into more and more energy-intensive production technologies. After presenting the introduction and review of the literature, the economic and energy efficiency of agriculture in the EU countries in 2016 was examined, which was at a high level – the DEA reached 0.67. Then, the correlation between the socio-economic development of countries and their economic and energy efficiency was analysed. It was also found that along with socio-economic development in the EU countries, the economic and energy efficiency of agriculture is increasing.

Efficiency And Productivity Analysis In Turkish Banking Sector With Data Envelopment Analysis And Malmquist Index

The aim of this study is to analyze the efficiency and productivity of 3 public, 6 private and 6 foreign deposit banks operating in the Turkish banking sector with the help of data envelopment analysis and Malmquist index.For this purpose, the efficiency of 15 deposit banks operating in the Turkish banking sector between 2014 and 2018 was measured and whether the efficiency of Malmquist productivity index changed over the years. In the study, input-oriented Charnes Cooper and Rhodes (CCR) model was used under the assumption of constant return to scale and 4 input 2 output variables were selected. Inputs in efficiency and productivity measurement are defined as; personnel expenses / total assets (%), total loans / total assets (%), equity / total assets (%), total deposits / total assets (%), outputs are defined as; the earning power of assets (net profit / total assets), the earning power of equity (net profit / equity) (%). The Windows Data Envelopment Analysis Program (Win4Deap) package program was used in the analysis and brokerage approach was adopted. While 4 banks were active under the Constant Return to Scale(CRS) assumption between 2014 and 2018, 8 banks were found active under the assumption of Variable Return to Scale (VRS).Inefficient banks; target values were calculated by slacks movement and radial movement values to their original values and it was found that the lambda values calculated by Win4Deap program and which banks are peers. The changes observed in Malmquist total factor productivity, technical efficiency, technological efficiency, pure efficiency, scale efficiency and total factor productivity were analyzed as a whole and decision-making units experienced improvement in the 2016-2017 period.

A Novel Machine Learning Approach Combined with Optimization Models for Eco-efficiency Evaluation

Machine learning approaches have been developed rapidly and also they have been involved in many academic findings and discoveries. Additionally, they are widely assessed in numerous industries such as cement companies. Cement companies in developing countries, despite many profits such as valuable mines, face many challenges. Optimization, as a key part of machine learning, has attracted more attention. The main purpose of this paper is to combine a novel Data Envelopment Analysis (DEA) approach in optimization at the first step to find the Decision-Making Unit (DMU) with innovative clustering algorithms in machine learning at the second step introduce the model and algorithm with higher accuracy. At the optimization section with converting two-stage to a simple standard single-stage model, 24 cement companies from five developing countries over 2014–2019 are compared. Window-DEA analysis is used since it leads to increase judgment on the consequences, mainly when applied to small samples followed by allowing year-by-year comparisons of the results. Applying window analysis can be beneficial for managers to expand their comparison and evaluation. To find the most accurate model CCR (Charnes, Cooper and Rhodes model), BBC (Banker, Charnes and Cooper model) and Free Disposal Hull (FDH) DEA model for measuring the efficiency of decision processes are used. FDH model allows the free disposability to construct the production possibility set. At the machine learning section, a novel three-layers data mining filtering pre-processes proposed by expert judgment for clustering algorithms to increase the accuracy and to eliminate unrelated attributes and data. Finally, the most efficient company, best performance model and the most accurate algorithm are introduced. The results indicate that the 22nd company has the highest efficiency score with an efficiency score of 1 for all years. FDH model has the highest efficiency scores during all periods compared with other suggested models. K-means algorithm receives the highest accuracy in all three suggested filtering layers. The BCC and CCR models have the second and third places, respectively. The hierarchical clustering and density-based clustering algorithms have the second and third places, correspondingly.