Showing papers on "Data envelopment analysis published in 2018"

A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016

Abstract: In recent years there has been an exponential growth in the number of publications related to theory and applications of Data Envelopment Analysis (DEA). Charnes, Cooper, and Rhodes (1978) introduced DEA as a tool for measuring efficiency and productivity of decision making units. DEA has immediately been recognized as a modern tool for performance measurement. Since then, a large and considerable amount of articles has been appeared, including significant breakthroughs in theory and a great portion of works on DEA applications, both public and private sectors, to assess the efficiency and productivity of their activities. Although there have been several bibliographic collections reported, a comprehensive analysis and listing of DEA-related articles covering its first four decades of history is still missing. This paper, thus, aims to report an extensive listing of DEA-related articles including theory and methodology developments and "real" applications in diversified scenarios from 1978 to end of 2016. Some summary statistics of the publications' growth, the most utilized academic journals, authorship analysis, as well as keywords analysis are also provided.

Better resource management: An improved resource and environmental efficiency evaluation approach that considers undesirable outputs

Abstract: Evaluating resource and environmental efficiency is helpful to stakeholders when making iterative improvements to resource management policies, with regards to either their business activities or to societal management. Traditional evaluation models—which consider undesirable outputs in measuring environmental efficiency—are output-oriented, and so they only pursue the minimum undesirable outputs and the maximum desirable outputs, based on current inputs. The growing depletion of nonrenewable resources makes it difficult to acquire the resources essential to socioeconomic development. In such circumstances, better consideration of resource use (i.e., inputs) is required when considering both desirable and undesirable outputs. This study aims to develop an improved approach by which to evaluate resource and environmental efficiency, based on data envelopment analysis; in this approach, the evaluation of resource inputs into the objective function is introduced. On account of its improvements, the new model can measure not only resource and environmental efficiency, but also efficiencies with regards to resource inputs, undesirable outputs, and desirable outputs. The feasibility of the model is verified through its use in undertaking further empirical analyses of data from mainland China’s 31 provinces. The results of that analysis show the average resource and environmental efficiency value of China’s inefficient provinces to be only 0.65—a value that suggests that mainland China’s resource and environmental efficiency needs to be further improved.

The debate on flexibility of environmental regulations, innovation capabilities and financial performance : a novel use of DEA

Abstract: Operational research models have been employed to understand development issues associated with environmental sustainability. This article describes a novel application of data envelopment analysis (DEA) to help extend a specific debate in the literature on Porter's hypothesis in environmental policy. The debate deals with the impact of flexibility of regulations on the relationship between innovation capabilities on financial performance in organisations. Using the resource based view of a firm, we hypothesise that relationship between innovation capabilities and financial performance in firms depends on how flexible or inflexible environmental regulations are. We apply DEA to capture the flexibility of environmental regulations. Our results indicate that innovation capabilities significantly influence financial performance of firms if firms feel that the environmental regulations they face are flexible and offer more freedom in meeting the requirements of regulations. On the other hand, corporations that feel that they face more inflexible regulations are not so effective in improving their financial performance with their innovation capabilities.

Equity and efficiency of primary health care resource allocation in mainland China

Abstract: China had proposed the unification of equity and efficiency since the launch of the new round of health system reform in 2009 And the central government gave priority to the development of primary health care (PHC) whilst ensuring its availability and improving its efficiency This study aimed to evaluate the changes of equity and efficiency in PHC resource allocation (PHCRA) and explored ways to improve the current situation The data of this study came from the China Health Statistical Yearbook (2013–2017) and China Statistical Yearbook (2017) Three and five indicators were used to measure equity and efficiency, respectively The Lorenz curve, Gini coefficient (G), Theil index (T) and health resource density index (HRDI) were used to assess equity in demographic and geographical dimensions Data envelopment analysis (DEA) and the Malmquist productivity index (MPI) were chosen to measure the efficiency and productivity of PHCRA From 2012 to 2016, the total amount of PHCR had increased year by year The Gs by population size were below 02 and that by geographical area were between 06 and 07 T had the same trend with G, and intra-regional contribution rates were higher than inter-regional contribution rates, which were all beyond 60% From 2012 to 2016, the numbers of provinces that achieved an effective DEA were 4, 3, 4, 5 and 5, respectively The mean of the total factor productivity index was 0994 The equity of PHCRA in terms of population size is superior in the geographical area Intra-regional differences are the main source of inequality The eastern region has the highest density of PHCR, whereas the western region has the lowest In addition, PHC institutions in more than 80% of the provinces are inefficient, and the productivity of the institutions decline by 06% from 2012 to 2016 because of technological retrogression

Multi-Criteria Decision Making (MCDM) Approaches for Solar Power Plant Location Selection in Viet Nam

Abstract: The ongoing industrialization and modernization period has increased the demand for energy in Viet Nam. This has led to over-exploitation and exhausts fossil fuel sources. Nowadays, Viet Nam’s energy mix is primarily based on thermal and hydro power. The Vietnamese government is trying to increase the proportion of renewable energy. The plan will raise the total solar power capacity from nearly 0 to 12,000 MW, equivalent to about 12 nuclear reactors, by 2030. Therefore, the construction of solar power plants is needed in Viet Nam. In this study, the authors present a multi-criteria decision making (MCDM) model by combining three methodologies, including fuzzy analytical hierarchy process (FAHP), data envelopment analysis (DEA), and the technique for order of preference by similarity to ideal solution (TOPSIS) to find the best location for building a solar power plant based on both quantitative and qualitative criteria. Initially, the potential locations from 46 sites in Viet Nam were selected by several DEA models. Then, AHP with fuzzy logic is employed to determine the weight of the factors. The TOPSIS approach is then applied to rank the locations in the final step. The results show that Binh Thuan is the optimal location to build a solar power plant because it has the highest ranking score in the final phase of this study. The contribution of this study is the proposal of a MCDM model for solar plant location selection in Viet Nam under fuzzy environment conditions. This paper also is part of the evolution of a new approach that is flexible and practical for decision makers. Furthermore, this research provides useful guidelines for solar power plant location selection in many countries as well as a guideline for location selection of other industries.

Application of data envelopment analysis models in supply chain management: a systematic review and meta-analysis

Abstract: Supply chain management aims to designing, managing and coordinating material/product, information and financial flows to fulfill the customer requirements at low costs and thereby increasing supply chain profitability. In the last decades, data envelopment analysis has become the main topic of interest as a mathematical tool to evaluate supply chain management. While, various data envelopment analysis models have been suggested to measure and evaluate the supply chain management, there is a lack of research regarding to systematic literature review and classification of study in this field. Regarding this, some major databases including Web of Science and Scopus have been nominated and systematic and meta-analysis method which called “PRISMA” has been proposed. Accordingly, a review of 75 published articles appearing in 35 scholarly international journals and conferences between 1996 and 2016 have been attained to reach a comprehensive review of data envelopment analysis models in evaluation supply chain management. Consequently, the selected published articles have been categorized by author name, the year of publication, technique, application area, country, scope, data envelopment analysis purpose, study purpose, research gap and contribution, results and outcome, and journals and conferences in which they appeared. The results of this study indicated that areas of supplier selection, supply chain efficiency and sustainable supply chain have had the highest frequently than other areas. In addition, results of this review paper indicated that data envelopment analysis showed great promise to be a good evaluative tool for future evaluation on supply chain management, where the production function between the inputs and outputs was virtually absent or extremely difficult to acquire. The facility of multiple inputs and multiple outputs of the data envelopment analysis model was definitely an attractive one to most researchers and, therefore, the data envelopment analysis procedure had found many applications beyond supply chain management into organization and industry.

China's regional tourism efficiency: A two-stage double bootstrap data envelopment analysis

Abstract: This paper investigates tourism efficiency and its determinants using a two-stage double bootstrap approach for a global panel of 31 Chinese provinces over the period 2008–2013. Bias-corrected data envelopment analysis (DEA) efficiency scores were first calculated by employing the smoothed homogeneous bootstrapped procedure ( Simar & Wilson, 2000 ). They were then regressed on a set of explanatory variables using the double-truncated regression approach. The results show that the tourism efficiency in China was low over the sample period. At the regional level, the average tourism efficiency in the east China was higher than central and west. The results also indicate that trade openness, climate change and intensity of market competition increase tourism efficiency. The study reveals several interesting and useful managerial insights and implications for tourism sector.

Multi-Expert Performance Evaluation of Healthcare Institutions Using an Integrated Intuitionistic Fuzzy AHP&DEA Methodology

Abstract: Healthcare management and healthcare industry have been one of the popular and complex topics that many researchers and professionals have focused on. This paper proposes a new multi-expert fuzzy approach integrating intuitionistic fuzzy Data Envelopment Analysis (DEA) and intuitionistic fuzzy Analytic Hierarchy Process (IF-AHP) for solving the performance evaluation problem of healthcare institutions. In this paper, intuitionistic fuzzy sets (IFS) have been preferred since they simultaneously provide information on the membership, non-membership, and hesitancy functions. A real life problem is demonstrated to validate the proposed methodology. A total number of 16 hospitals operating in Istanbul have been analyzed based on a broad set of inputs and outputs. Then, a comparison with crisp DEA has been performed.

Healthcare efficiency assessment using DEA analysis in the Slovak Republic.

Abstract: A regional disparity is becoming increasingly important growth constraint. Policy makers need quantitative knowledge to design effective and targeted policies. In this paper, the regional efficiency of healthcare facilities in Slovakia is measured (2008–2015) using data envelopment analysis (DEA). The DEA is the dominant approach to assessing the efficiency of the healthcare system but also other economic areas. In this study, the window approach is introduced as an extension to the basic DEA models to evaluate healthcare technical efficiency in individual regions and quantify the basic regional disparities and discrepancies. The window DEA method was chosen since it leads to increased discrimination on results especially when applied to small samples and it enables year-by-year comparisons of the results. Two stable inputs (number of beds, number of medical staff), three variable inputs (number of all medical equipment, number of magnetic resonance (MR) devices, number of computed tomography (CT) devices) and two stable outputs (use of beds, average nursing time) were chosen as production variable in an output-oriented 4-year window DEA model for the assessment of technical efficiency in 8 regions. The database was made available from the National Health Information Center and the Slovak Statistical Office, as well as from the online databases Slovstat and DataCube. The aim of the paper is to quantify the impact of the non-standard Data Envelopment Analysis (DEA) variables as the use of medical technologies (MR, CT) on the results of the assessment of the efficiency of the healthcare facilities and their adequacy in the evaluation of the monitored processes. The results of the analysis have shown that there is an indirect dependence between the values of the variables over time and the results of the estimated efficiency in all regions. The regions that had low values of the variables over time achieved a high degree of efficiency and vice versa. Interesting knowledge was that the gradual addition of variables number of MR, number of CT and number of medical devices together, to the input side did not have a significant impact on the overall estimated efficiency of healthcare facilities.

Two-stage DEA-Truncated Regression

Abstract: DEA-Double Bootstrapped Truncated Regression is proposed for banking efficiency.We evaluate risk variables as determinants of European domestic banks efficiency.The impact of financial development in the countries under study is examined.The results indicate the existence of a periphery banks efficiency meta-frontier.Banks efficiency is more affected in lower levels of financial development. This study evaluates the efficiency of peripheral European domestic banks and examines the effects of bank-risk determinants on their performance over 20072014. Data Envelopment Analysis is utilised on a Malmquist Productivity Index in order to calculate the bank efficiency scores. Next, a Double Bootstrapped Truncated Regression is applied to obtain bias-corrected scores and examine whether changes in the financial conditions affect differently banks efficiency levels. The analysis accounts for the sovereign debt crisis period and for different levels of financial development in the countries under study. Such an application in the respective European banking setting is unique. The proposed method also copes with common misspecification problems observed in regression models based on efficiency scores. The results have important policy implications for the Euro area, as they indicate the existence of a periphery efficiency meta-frontier. Liquidity and credit risk are found to negatively affect banks productivity, whereas capital and profit risk have a positive impact on their performance. The crisis period is found to augment these effects, while bank-risk variables affect more banks' efficiency when lower levels of financial development are observed.

A methodology for project portfolio selection under criteria prioritisation, uncertainty and projects interdependency – combination of fuzzy QFD and DEA

Abstract: Resources of an organisation (people, time, money, equipment, etc) are never endless. As such, a constant and continuous challenge for decision makers is to decide which projects should be given priority in terms of receiving critical resources in a way that the organisation's productivity and profitability is best guaranteed. Previous literature has already developed a plenitude of project portfolio selection methodologies ranging from simple scoring to complex mathematical models. However, most of them too often fail to propose one integrated and seamless method that can simultaneously take into account three important elements: (1) prioritisation of selection criteria over each other, (2) uncertainty in decision-making, and (3) projects interdependencies. This paper aims to fill this gap by proposing an integrated method that can simultaneously address all these three aspects. The proposed method combines Quality Function Development (QFD), fuzzy logic, and Data Envelopment Analysis (DEA) to accounts for prioritisation, uncertainty and interdependency. We then apply this method in a numerical example from a real world case to illustrate the applicability and efficacy of the proposed methodology.

Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange

Abstract: One of the main features to invest in stock exchange companies is their financial performance. On the other hand, conventional evaluation methods such as data envelopment analysis are not only a retrospective process, but are also a process, which are incomplete and ineffective approaches to evaluate the companies in the future. To remove this problem, it is required to plan an expert system for evaluating organizations when the online data are received from stock exchange market. This paper deals with an approach for predicting the online financial performance of companies when data are received in different time’s intervals. The proposed approach is based on integrating fuzzy C-means (FCM), data envelopment analysis (DEA) and artificial neural network (ANN). The classical FCM method is unable to update the number of clusters and their members when the data are changed or the new data are received. Hence, this method is developed in order to make dynamic features for the number of clusters and clusters members in classical FCM. Then, DEA is used to evaluate DMUs by using financial ratios to provide targets in neural network. Finally, the designed network is trained and prepared for predicting companies’ future performance. The data on Tehran Stock Market companies for six consecutive years (2007–2012) are used to show the abilities of the proposed approach.

Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends

Abstract: Measurement of environmental and energy economics presents an analytical foundation for environmental decision making and policy analysis. Applications of data envelopment analysis (DEA) models in the assessment of environmental and energy economics are increasing notably. The main objective of this review paper is to provide the comprehensive overview of the application of DEA models in the fields of environmental and energy economics. In this regard, a total 145 articles published in the high-quality international journals extracted from two important databases (Web of Science and Scopus) were selected for review. The 145 selected articles are reviewed and classified based on different criteria including author(s), application scheme, different DEA models, application fields, the name of journals and year of publication. This review article provided insights into the methodological and conceptualization study in the application of DEA models in the environmental and energy economics fields. This study should enable scholars and practitioners to understand the state of art of input and output indicators of DEA in the fields of environmental and energy economics.

Allocating a fixed cost based on a DEA-game cross efficiency approach

Abstract: In many real managerial applications, an issue of considerable importance is allocating a total fixed cost across a set of competing decision making units (DMUs). The fixed cost allocation problem has also become one of the most important applications of the data envelopment analysis (DEA) methodology. In this paper, we will approach the fixed cost allocation problem by explicitly considering both competition and cooperation relationships among DMUs. To this end, we integrate cooperative game theory and the cross efficiency method to develop a DEA-game cross efficiency approach to generate a unique and fair allocation plan. With the proposed approach, each DMU is considered as a player and a super-additive characteristic function is defined for coalitions of DMUs. Then, the Shapley value is calculated for each DMU and accordingly associated common weights are optimized to determine the final allocation plan. Since the cross efficiency method considers peer appraisal and the cooperative game theory allows for equitable negotiations, all DMUs are supposed to reach a consensus on the equitable allocation scheme through our novel approach. From this perspective, our proposed approach is promising and attractive for allocating a fixed cost in large organizations. Finally, the DEA-game cross efficiency approach is demonstrated with a numerical example derived from previous literature and the results are compared to some existing methods. Additionally, we apply the proposed approach to an empirical application concerning city commercial bank activities in China.