Showing papers on "Data envelopment analysis published in 2016"
TL;DR: In this paper, an overview of the literature of WWTP energy-use performance and of the state-of-the-art methods for energy benchmarking is given, along with a large dataset of WET energy consumption data, together with the methods for synthesizing the information.
315 citations
TL;DR: Wang et al. as discussed by the authors introduced an improved Malmquist-Luenberger productivity index to measure the green productivity growth of China's manufacturing sector during the 11th five-year period (2006-2010).
292 citations
01 Jan 2016
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241 citations
TL;DR: A comprehensive survey of empirical studies published in 2006-2015 on China's regional energy efficiency and carbon emission efficiency assessment using DEA-type models was conducted in this paper, where the main features used in previous studies were identified, and then the methodological framework for deriving the EE&CE indicators as well as six widely used DEA models were introduced.
224 citations
TL;DR: This study applies a network clustering method to group the literature through a citation network established from the DEA literature over the period 2000 to 2014, and presents the research fronts, a coherent topic or issue addressed by a group of research articles in recent years.
Abstract: Research activities relating to data envelopment analysis (DEA) have grown at a fast rate recently. Exactly what activities have been carrying the research momentum forward is a question of particular interest to the research community. The purpose of this study is to find these research activities, or research fronts, in DEA. A research front refers to a coherent topic or issue addressed by a group of research articles in recent years. The large amount of DEA literature makes it difficult to use any traditional qualitative methodology to sort out the matter. Thus, this study applies a network clustering method to group the literature through a citation network established from the DEA literature over the period 2000 to 2014. The keywords of the articles in each discovered group help pinpoint its research focus. The four research fronts identified are “bootstrapping and two-stage analysis”, “undesirable factors”, “cross-efficiency and ranking”, and “network DEA, dynamic DEA, and SBM”. Each research front is then examined with key-route main path analysis to uncover the elements in its core. In addition to presenting the research fronts, this study also updates the main paths and author statistics of DEA development since its inception and compares them with those reported in a previous study.
217 citations
TL;DR: This paper provides a review of stochastic Data Envelopment Analysis (DEA) and discusses extensions of deterministic DEA in three directions: deviations from the deterministic frontier are modeled as stochastically variables, random noise in terms of measurement errors, sample noise, and specification errors is made an integral part of the model.
183 citations
TL;DR: In this article, a survey and combination of existing results from the stochastic frontier literature and the classic simultaneous equations literature is presented, together with some new results from Simultaneous Equations.
174 citations
TL;DR: Wang et al. as discussed by the authors applied data envelopment analysis (DEA) to measure the energy and environment performance of transportation systems in China with the goal of sustainable development, treating transportation as a parallel system consisting of subsystems for passenger transportation and freight transportation, and extending a parallel DEA approach to evaluate the efficiency of each subsystem.
Abstract: Because of China’s rapid economic development, its transportation system has become one of China’s high-energy-consumption and high-pollution-emission sectors. However, little research has been done which pays close attention to China’s transportation system, especially in terms of energy and environmental efficiency evaluation. In this paper, data envelopment analysis (DEA) is applied to measure the energy and environment performance of transportation systems in China with the goal of sustainable development. This paper treats transportation as a parallel system consisting of subsystems for passenger transportation and freight transportation, and extends a parallel DEA approach to evaluate the efficiency of each subsystem. An efficiency decomposition procedure is proposed to obtain the highest achievable subsystem efficiency. Our empirical study on 30 of mainland China’s provincial-level regions shows that most of them have a low efficiency in their transportation system and the two parallel subsystems. There are large efficiency differences between the passenger and freight transportation subsystems. In addition, unbalanced development has occurred in the three large areas of China, with the east having the highest efficiency, followed by central China and then west. Therefore, more measures should be taken to balance and coordinate the development between the three large areas and between the two subsystems within them. Our analysis approach gives data for determining effective measures.
168 citations
TL;DR: New Fuzzy-DEA α-level models to assess underlying uncertainty are proposed and price of labor, price of capital, and market-share were found to be the significant factors in measuring bank efficiency.
135 citations
TL;DR: This study considers an X-bar control chart design problem with multiple and often conflicting objectives, including the expected time the process remains in statistical control status, the type-I error, and the detection power, and applies multi-objective optimization methods founded on the reference-points-based non-dominated sorting genetic algorithm-II and MOPSO to efficiently solve the optimization problem.
Abstract: We use NSGA-III and MOPSO algorithms to solve a multi-objective X-bar control chart design problem.NSGA-III and MOPSO are modified to handle a constrained multi-objective problem with discrete and continuous variables.Four DEA models are proposed to reduce the number of Pareto optimal solutions to a manageable size.TOPSIS is used to prioritize the efficient optimal solutions.Several metrics are used to compare the performance of NSGA-III and MOPSO algorithms. X-bar control charts are widely used to monitor and control business and manufacturing processes. This study considers an X-bar control chart design problem with multiple and often conflicting objectives, including the expected time the process remains in statistical control status, the type-I error, and the detection power. An integrated multi-objective algorithm is proposed for optimizing economical control chart design. We applied multi-objective optimization methods founded on the reference-points-based non-dominated sorting genetic algorithm-II (NSGA-III) and a multi-objective particle swarm optimization (MOPSO) algorithm to efficiently solve the optimization problem. Then, two different multiple criteria decision making (MCDM) methods, including data envelopment analysis (DEA) and the technique for order of preference by similarity to ideal solution (TOPSIS), are used to reduce the number of Pareto optimal solutions to a manageable size. Four DEA methods compare the optimal solutions based on relative efficiency, and then the TOPSIS method ranks the efficient optimal solutions. Several metrics are used to compare the performance of the NSGA-III and MOPSO algorithms. In addition, the DEA and TOPSIS methods are used to compare the performance of NSGA-III and MOPSO. A well-known case study is formulated and solved to demonstrate the applicability and exhibit the efficacy of the proposed optimization algorithm. In addition, several numerical examples are developed to compare the NSGA-III and MOPSO algorithms. Results show that NSGA-III performs better in generating efficient optimal solutions.
133 citations
TL;DR: In this paper, a non-radial ZSG-DEA model was used to allocate CO 2 emissions between different Chinese provinces, which can better reflect macro-production process.
TL;DR: GP provides a robust nonlinear mathematical equation for the suppliers’ efficiency using the determined criteria and is integrated with a new AI approach namely genetic programming (GP) to overcome the shortcomings of previous DEA–AI models in supplier selection.
Abstract: Supplier evaluation plays a critical role in a successful supply chain management. Hence, the evaluation and selection of the right suppliers have become a central decision of manufacturing business activities around the world. Consequently, numerous individual and integrated methods have been presented to evaluate and select suppliers. The current literature shows that hybrid artificial intelligence (AI)-based models have received much attention for supplier evaluation. Integrated data envelopment analysis---artificial neural network (DEA---ANN) is one of the combined methods that have recently garnered great attention from academics and practitioners. However, DEA---ANN model has some drawbacks, which make some limitation in the evaluation process. In this study, we aim at improving the previous DEA---AI models by integrating the Kourosh and Arash method as a robust model of DEA with a new AI approach namely genetic programming (GP) to overcome the shortcomings of previous DEA---AI models in supplier selection. Indeed, in this paper, GP provides a robust nonlinear mathematical equation for the suppliers' efficiency using the determined criteria. To validate the model, adaptive neuro-fuzzy inference system as a powerful tool was used to compare the result with GP-based model. In addition, parametric analysis and unseen data set were used to validate the precision of the model.
TL;DR: An approach for analyzing the reuse of undesirable intermediate outputs in a two-stage production process with a shared resource is provided and a heuristic algorithm is suggested to transform the nonlinear model into a parametric linear one.
TL;DR: In this article, the authors analyzed the production performance of hospital services in Ontario (Canada), by investigating its key determinants, such as occupancy rate, rate of unit-producing personnel, outpatient-inpatient ratio, case-mix index, geographic locations, size and teaching status.
Abstract: In this work, we analyze production performance of hospital services in Ontario (Canada), by investigating its key determinants. Using data for the years 2003 and 2006, we follow the two-stage approach of Simar and Wilson (2007) [76]. Specifically, we use Data Envelopment Analysis (DEA) at the first stage to estimate efficiency scores and then use truncated regression estimation with double-bootstrap to test the significance of explanatory variables. We also examine distributions of efficiency across geographic locations, size and teaching status. We find that several organizational factors such as occupancy rate, rate of unit-producing personnel, outpatient–inpatient ratio, case-mix index, geographic locations, size and teaching status are significant determinants of efficiency.
TL;DR: The main aim of the paper is to present an integrated assessment and classification framework for national and regional innovation efficiency, based on Data Envelopment Analysis and is formulated as a multiobjective mathematical program in order to consider the objectives and constraints of the different stages and levels of the innovation process.
Abstract: A framework for estimating national and regional innovation efficiency is presentedThe proposed DEA-based model is formulated as a multiobjective mathematical programMultiple objectives refer to different stages and hierarchies of innovation systemsOrdinal regression analysis examines the influence of additional variablesEfficiency results show significant differences across countries and regions Evaluating the efficiency of innovation systems can serve as a substantial enabling tool for policymaking serving to identify best practices and develop potential improvements of actions and strategies. It also serves to provide valuable insight in understanding the nature and dynamics of innovation process at its different stages and levels. The main aim of the paper is to present an integrated assessment and classification framework for national and regional innovation efficiency. The proposed model is based on Data Envelopment Analysis and is formulated as a multiobjective mathematical program in order to consider the objectives and constraints of the different stages and levels of the innovation process. Additionally, the model copes with DEA inconsistencies when ratio measures are employed. In the second part of the study, a multicriteria decision aid approach, based on an ordinal regression model, is applied in order to study how environmental factors on innovation and entrepreneurship affect the estimated efficiency scores. The proposed approach is applied to a set of 23 European countries and their 185 corresponding regions. The results show that there are large differences regarding the efficiency scores of the different stages and levels, implying the existence of significant divergences from the expected norm concerning innovation efficiency. The contribution of the paper lies (i) in the proposed multiobjective model, which is able to model the multiple stages and levels of the innovation process and handle ratio measures without requiring the same set of inputs and outputs at different levels and (ii) in the presented application of the model in the efficiency evaluation of innovation systems, including a meta-analysis of the results based on the framework of the Quadruple Innovation Helix. Such an approach may provide a valuable tool for country/region comparison and policy formulation.
TL;DR: In this article, a global Malmquist-Luenberger productivity index (GMLPI) was proposed to measure eco-efficiency with CO2 emissions in Chinese manufacturing industries.
TL;DR: If the proposed algorithm terminates at its step 3, the evaluation results generated by the approach unify self-evaluated, peer-evaluation, and common-weight-evaluations in DEA cross-efficiency evaluation will be more likely to be accepted by all the DMUs.
TL;DR: This paper evaluates for the first time the efficiency of a sample of WWTPs by applying the weighted slacks-based measure model, a non-radial DEA model which allows assigning weights to the inputs and outputs according their importance.
TL;DR: In this paper, the authors use data envelopment analysis (DEA) to determine the trade-offs inherent in managing the triple bottom line of profits, people and the planet.
Abstract: Corporate social responsibility (CSR) has become a mandate for strategic managers and is often an important element of a differentiation strategy, but there is little research on how managers can make socially responsible decisions within the context of competitive strategy. In this study we explain how data envelopment analysis (DEA) results can be used to determine the trade-offs inherent in managing the triple bottom line of profits, people and the planet. Once the trade-offs are well understood, managers can implement sustainable competitive strategies that incorporate socially responsible decisions. Using public data from the electric power generation industry, we demonstrate how DEA can be utilized to determine the trade-offs between efficiency, costs and pollution reduction, allowing managers to make and champion socially responsible decisions. We discuss the general applicability of our method for making strategic decisions incorporating the triple bottom line. Copyright © 2014 John Wiley & Sons, Ltd and ERP Environment
TL;DR: A unified simulation-based algorithm is developed to solve the problem of passenger flow organization in subway stations under uncertain demand and data envelopment analysis (DEA) and genetic algorithms (GA) are embedded in this algorithm to increase computing speed.
Abstract: This paper proposes a problem of passenger flow organization in subway stations under uncertain demand. The existing concepts of station service capacity are extended and further classified into three in different demand scenarios. Mathematical models are put forward to measure the three capacities and a unified simulation-based algorithm is developed to solve them. To increase computing speed, data envelopment analysis (DEA) and genetic algorithms (GA) are embedded in this algorithm. A case study will demonstrate the performance of the proposed algorithm and give a detailed procedure of passenger flow control based on station service capacity in various demand scenarios.
TL;DR: In this paper, the authors evaluated the energy efficiency trends of five energy intensive industries in 23 European Union (EU) countries over the period 2000-2009 and found that an overall improvement in efficiency was observed in all sectors over the time period.
TL;DR: In this paper, the authors present an approach to estimate unique and unbiased efficiency scores for the individual stages, which are then composed to obtain the efficiency of the overall system, by selecting the aggregation method a posteriori.
Abstract: A two-stage production process assumes that the first stage transforms external inputs to a number of intermediate measures, which then are used as inputs to the second stage that produces the final outputs. The fundamental approaches to two-stage network data envelopment analysis are the multiplicative and the additive efficiency-decomposition approaches. Both they assume a series relationship between the two stages but they differ in the definition of the overall system efficiency as well as in the way they conceptualize the decomposition of the overall efficiency to the efficiencies of the individual stages. In this paper, we first show that the efficiency estimates obtained by the additive decomposition method are biased, by unduly favouring one stage against the other, while those obtained by the multiplicative method are not unique. Then, we present a novel approach to estimate unique and unbiased efficiency scores for the individual stages, which are then composed to obtain the efficiency of the overall system, by selecting the aggregation method a posteriori. Within the particularity of two-stage processes emerging from the conflicting role of the intermediate measures, we develop an envelopment model to locate the efficient frontier whose derivation from our primal multiplier efficiency assessment model is effectively justified. The results derived from our approach are compared with those obtained by the aforementioned basic methods on experimental data as well as on test data drawn from the literature. Similarities and dissimilarities in the results are rigorously justified.
TL;DR: Wang et al. as mentioned in this paper introduced a novel Malmquist-Luenberger productivity (MLP) index based on directional distance function (DDF) to address the issue of productivity evolution of DMUs in the presence of undesirable outputs.
TL;DR: In this article, the authors measured and analyzed energy efficiency in the countries that comprise the BRICS group (Brazil, Russia, India, China and South Africa) and the G7 group (Canada, France, Germany, Italy, Japan, United Kingdom and United States) considering the total-factor structure.
TL;DR: In this article, the authors evaluate the eco-efficiency performance of various subsectors in the Australian agri-food systems through the use of input-output-oriented approaches of data envelopment analysis and material flow analysis.
Abstract: The food industry in Australia (agriculture and manufacturing) plays a fundamental role in contributing to socioeconomic sectors nationally. However, alongside the benefits, the industry also produces environmental burdens associated with the production of food. Sectorally, agriculture is the largest consumer of water. Additionally, land degradation, greenhouse gas emissions, energy consumption, and waste generation are considered the main environmental impacts caused by the industry. The research project aims to evaluate the eco-efficiency performance of various subsectors in the Australian agri-food systems through the use of input-output–oriented approaches of data envelopment analysis and material flow analysis. This helps in establishing environmental and economic indicators for the industry. The results have shown inefficiencies during the life cycle of food production in Australia. Following the principles of industrial ecology, the study recommends the implementation of sustainable processes to increase efficiency, diminish undesirable outputs, and decrease the use of nonrenewable inputs within the production cycle. Broadly, the research outcomes are useful to inform decision makers about the advantages of moving from a traditional linear system to a circular production system, where a sustainable and efficient circular economy could be created in the Australian food industry.
TL;DR: In this paper, the authors use central limit theorem results from their previous work to develop additional theoretical results permitting consistent tests of model structure and provide Monte Carlo evidence on the performance of the tests in terms of size and power.
Abstract: Data envelopment analysis (DEA) and free disposal hull (FDH) estimators are widely used to estimate efficiency of production. Practitioners use DEA estimators far more frequently than FDH estimators, implicitly assuming that production sets are convex. Moreover, use of the constant returns to scale (CRS) version of the DEA estimator requires an assumption of CRS. Although bootstrap methods have been developed for making inference about the efficiencies of individual units, until now no methods exist for making consistent inference about differences in mean efficiency across groups of producers or for testing hypotheses about model structure such as returns to scale or convexity of the production set. We use central limit theorem results from our previous work to develop additional theoretical results permitting consistent tests of model structure and provide Monte Carlo evidence on the performance of the tests in terms of size and power. In addition, the variable returns to scale version of the DEA estima...
TL;DR: In this paper, the authors report an analysis of hotel profitability using Data Envelopment Analysis (DEA) and the Return On Assets (ROA) analysis, which is shown to be important when holding geographical and operating contracts as constants.
TL;DR: In this paper, the authors use distance functions to define new output and input quantity indexes that satisfy important axioms from index number theory (e.g., identity, transitivity, proportionality and time-space reversal).
TL;DR: This work first incorporates a target identification model to get reachable targets for all DMUs, then several secondary goal models are proposed for weights selection considering both desirable and undesirable cross-efficiency targets of all the DMUs.
TL;DR: Using a non-radial slack-based measurement data envelopment analysis (SBM-DEA) model and state-level data, the authors assesses the environmental efficiency of the transportation sector in the U.S. from years 2004 to 2012.
Abstract: Sustainable transportation in the U.S. is essential for long-term economic growth and mobility, and environmental preservation. Using a non-radial slack-based measurement data envelopment analysis (SBM-DEA) model and state-level data, this study assesses the environmental efficiency of the transportation sector in the U.S. from years 2004 to 2012. In addition to environmental efficiency, carbon efficiency and potential carbon reduction were estimated for the 50 U.S. states. The findings of this study reveal that U.S. transportation sector was environmentally inefficient; U.S. states had an average transportation environmental efficiency score below 0.64. Therefore the states could substantially reduce carbon emissions to improve the environmental efficiency of their transportation sectors.