Generalized Data Envelopment Analysis Research Articles

Examining the efficiency of regional university technology transfer in China: A mixed-integer generalized data envelopment analysis framework

In university technology transfer performance assessment, some variables, such as labor and contracts, must be treated as integer-valued variables. Usually, the reference points obtained by the conventional data envelopment analysis (DEA) method are unrealistic when one of the input/output measures is an integer value. As a result, several integer DEA models have been proposed over the last decade. However, these models are based only on self-evaluation. In some cases, when a large gap exists between the best- and worst-performing units, the improvement path based on self-evaluation can be inappropriate. To overcome this issue, we propose a mixed-integer generalized DEA model based on peer evaluation to evaluate the efficiency level of regional university technology transfer in China. A sample of 27 provinces in China from 2008 to 2015 is used in the study. The results reveal that the eastern region, on average, obtained the highest level of efficiency among the three regions in China, whereas the western region presented a poor efficiency level. This is consistent with the current stage of development. Several recommendations are suggested for promoting technology transfer in the western region.

A generalized data envelopment analysis approach for fixed cost allocation with preference information

Exploring a commonly acceptable fixed cost allocation (FCA) scheme is a challenging yet significant task, since one may contend with conflicting objectives. In this paper, we propose a new data envelopment analysis based FCA technique. The proposed approach aims to balance individual efficiency guarantees and collective preference objectives simultaneously. Particularly, a preference value loss (PVL) index is explicitly constructed to quantify the impact of preference considerations. Moreover, our generalized FCA strategy guarantees a minimal PVL and provides a reasonable and realizable lexicographic evaluation procedure to single out the final FCA scheme. Interestingly, the efficiency invariance and efficiency variance approaches from the prior literature are special cases of our proposed approach. Finally, the proposed approach is validated with a numerical example and an empirical application to bank maintenance fee distribution.

The grain Food-Energy-Water nexus in China: Benchmarking sustainability with generalized data envelopment analysis

Food insecurity can be considered as a significant cause to instability in some regions around the world. Grain production utilizes a multiple of inputs, such as: water resources, fertilizers, pesticides, energy, machinery, and labor. In China, grain production has led to huge irrigation water use, non-point source pollution, and greenhouse gas emissions. It is necessary to emphasize the synergy between food production and ecological environment. In this study, a grain Food-Energy-Water nexus is delivered and an eco-efficiency sustainability evaluation metric is introduced, Sustainability of Grain Inputs (SGI), for investigating the sustainability of water and energy use in grain production across China. SGI is constructed by using generalized data envelopment analysis to comprehensively incorporate differences of water and energy inputs (including indirect energy use contained in agricultural chemicals such as fertilizers, pesticides, agricultural film, and direct energy use such as the electricity and diesel used for irrigation and agricultural machinery) in different regions across China. Both water and energy are considered by the new metric at the same time, which is built on the single resources metrices that are often used in the sustainability literature. This study evaluates the water and energy use of wheat and corn production in China. Wheat production uses water and energy sustainably in Sichuan, Shandong, and Henan; Corn production has the highest combined sustainability index in Shandong, Jilin, Liaoning, and Henan. In these areas, the grain sown area could be increased. However, wheat production in Inner Mongolia and corn production in Xinjiang rely on unsustainable water and energy inputs, and their grain sown areas could be reduced. The SGI is a tool that researchers and policy makers can use to better quantify the sustainability of water and energy inputs to grain production. It facilitates formulating policies about water saving and carbon emission reduce of grain production.

A goal-oriented approach for supporting input/output determination in regulatory benchmarking

Benchmarking, in many cases by means of Data Envelopment Analysis, is one of the main strategies of regulatory agencies to control the costs of monopolistic companies. In such cases, the determination of inputs and outputs as performance criteria is often an issue. Against this background, we propose a goal-oriented approach that allows to make the so-called Generalized Data Envelopment Analysis (GDEA) fruitful for regulatory benchmarking. As example, we apply this approach to the Brazilian electric energy transmission system operators. As policy implications, we provide directions and discussions on how to apply GDEA in regulatory benchmarking.

Measuring Energy Efficiency Performance in China: Do Technological and Environmental Concerns Matter for Energy Efficiency?

Considering the significance of green governance in economic restructuring and the green technology revolution, this study examines the impact of China’s recent green governance policies and their implications in various regions; it also examines new models, methods, and development directions for China’s green governance in the future. Green governance efficiency and spatial patterns have been studied through 2008–2018 data using a three-stage generalized panel Data Envelopment Analysis (DEA) model, spatial autocorrelation model, spatial gravity model, and social network analysis. The study summarizes the status and role of each provincial region in green governance based on the social network of green governance efficiency under the network architecture. It concludes that (1) green governance efficiency in China’s provinces has a U-shaped trend, with non-managerial elements in the external environment masking genuine green governance efficiency. (2) During the study period, the overall efficiency of the industrial system improved. The efficiency of the manufacturing and wastewater stages has been substantially enhanced, but no significant gains were observed in the treatment stages of solid and gas waste. (3) Although China has made progress in enhancing the overall efficiency of its industrial system, there is still potential for improvement. (4) China has established a “three horizontal and two vertical grid-type” green governance spatial correlation structure among the sub-stages of the industrial system, and the radiation impact of major provincial areas would increase overall green governance efficiency.

Material Selection in Green Design: A Method Combining DEA and TOPSIS

In order to rationalize material selection in green design, this study presents an attempt to combine the methods of generalized Data Envelopment Analysis (DEA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). By establishing a green material index system, the G-CCR model of generalized DEA was first used to select effective materials from the candidate samples, and TOPSIS was then used to sort the effective suppliers. The combined DEA/TOPSIS model helps to rank the materials by quality, and then integrate both the merits ofG-CCR model and the key characteristics of TOPSIS. The results of this study showed that the combined DEA/TOPSIS model can screen and exclude materials with poor performance when selecting wood for the furniture industry. Therefore, the combined model that is presented in this study provides a more rational and evidentiary basis for material selection in green design.

Multi-objective optimization using statistical models

In this paper we consider multi-objective optimization problems (MOOP) from the point of view of Bayesian analysis. MOOP problems can be considered equivalent to certain statistical models associated with the specific objectives and constraints. MOOP that can explore accurately the Pareto frontier are Generalized Data Envelopment Analysis and Goal Programming. In turn, posterior analysis of their associated statistical models can be implemented using Markov Chain Monte Carlo (MCMC) simulation. In addition, we consider the minimax regret problem which provides robust solutions and we develop similar MCMC posterior simulators without the need to define scenarios. The new techniques are shown to work well in four examples involving non-convex and disconnected Pareto problems and to a real world portfolio optimization problem where the purpose is to optimize simultaneously average return, mean absolute deviation, positive and negative skewness of portfolio returns. Globally minimum regret can also be implemented based on post-processing of MCMC draws.

Generalized DEA: an approach for supporting input/output factor determination in DEA

Purpose The determination of input and output factors is a well-known source of pitfalls when applying data envelopment analysis (DEA). The purpose of this paper is to contribute to overcome the respective problems of input/output factor determination related to factor selection, dual-role factors and undesirable factors. Design/methodology/approach The problems of input/output factor determination are discussed from a goal-oriented perspective, shedding a new light on the role of input/output factors in DEA. This is exemplified by the case of measuring pharmacy stores’ efficiency concerning their goal of customer retention. Findings The findings suggest to applying a generalized DEA (GDEA). The three steps of this approach include the development of a system of objectives, the derivation of corresponding performance criteria as well as the construction of cost and benefit functions. These functions build the basis for GDEA models, of which one is exemplarily described and applied to the customer retention case. Research limitations/implications While traditional DEA implicitly assumes linear cost and benefit functions, GDEA requires to explicitly specifying these functions. In doing so, the approach contributes to solve the problem of factor selection, the problem of dual-role factors and the problem of undesirable factors. Practical implications For determining input/output factors in a consistent and transparent manner, it is recommended to apply GDEA in practical benchmarking studies. Originality/value GDEA integrates well-known concepts of multi-criteria decision making into traditional DEA. The new approach helps to cope with the challenges of input/output factor determination in DEA.

Interaction between output efficiency and environmental efficiency: evidence from the textile industry in Jiangsu Province, China

Environmental efficiency improvement has played a crucial role in the theory and practice of stimulating clean production. This paper analyzes the interaction between environmental efficiency and output efficiency, particularly whether they reinforce each other or compete with each other, on the basis of a data set of 137 firms in the textile industry in China's Jiangsu Province. In the first stage, generalized data envelopment analysis is applied to calculate efficiency measures of energy, waste water, waste gas, soot, and output efficiency taking capital, labor, water, and energy as inputs, industrial output value as desirable output, and waste water discharges, waste gas and soot emissions as undesirable outputs. In the second stage analysis, a structural equation model with latent variables is applied to analyze the interaction between the latent variable environmental efficiency, measured by the four observed environmental indicators, and output efficiency, taking also into account the endogenous variable profit. The main outcomes of the structural equation model are the following. Firstly, environmental efficiency negatively impacts on profit while profit positively impacts on environmental efficiency. In a similar vein, output efficiency is found to depress profit while profit increases output efficiency. Thirdly, environmental efficiency has a positive impact on output efficiency while there is no effect of output efficiency on environmental efficiency. Fourthly, taxes impair a firm's output efficiency. From the findings it follows that a swap of general taxes for an energy tax is likely to improve both output efficiency and energy efficiency. The latter outcome implies a win–win situation which will facilitate the further implementation and adoption of environmental policy. Finally, the paper illustrates the applicability of structural equation modeling in efficiency analysis.

Multi-objective simulation optimization for selection and determination of order quantity in supplier selection problem under uncertainty and quality criteria

Simulation optimization is providing solutions to practical stochastic problems. Supplier selection is one of the most important decisions that determine the survival of an organization. In this paper, a novel multi-objective simulation optimization method to make decisions on selecting the suppliers and determining the order quantities is proposed. Regarding the fact that a real supply chain is multi-objective with uncertain parameters and includes both quantitative and qualitative variables, the proposed method considers these points and is applicable to real-world problems. This method also considers supplier selection and order quantity allocation to each supplier, which are totally related, as an integrated model. The proposed method consists of four basic modules: Cuckoo Optimization Algorithm (COA), Discrete Event Simulation (DES), Supply Chain Model (SCM), and Generalized Data Envelopment Analysis (GDEA). Unlike many multi-objective methods, the proposed method is not limited to the number of objective functions and this is one of its main benefits. It also pays attention to the efficiency of the organization and, at the same time, finding inputs which result in best output amounts. This method, in addition to the convergence criterion, pays special attention to the dispersion of the Pareto frontier as the second criterion for choosing the good solutions. For implementation of the proposed method, the numerical results for the problem of supplier selection in multi-product, multi-customer modes, and uncertain and qualitative variables are discussed and the Pareto frontiers are presented. The proposed method in this paper is compared with a similar method, and the results show the efficiency of the proposed method.

A fuzzy expected value approach under generalized data envelopment analysis

Fuzzy data envelopment analysis (DEA) models emerge as another class of DEA models to account for imprecise inputs and outputs for decision making units (DMUs). Although several approaches for solving fuzzy DEA models have been developed, there are some drawbacks, ranging from the inability to provide satisfactory discrimination power to simplistic numerical examples that handles only triangular fuzzy numbers or symmetrical fuzzy numbers. To address these drawbacks, this paper proposes using the concept of expected value in generalized DEA (GDEA) model. This allows the unification of three models – fuzzy expected CCR, fuzzy expected BCC, and fuzzy expected FDH models – and the ability of these models to handle both symmetrical and asymmetrical fuzzy numbers. We also explored the role of fuzzy GDEA model as a ranking method and compared it to existing super-efficiency evaluation models. Our proposed model is always feasible, while infeasibility problems remain in certain cases under existing super-efficiency models. In order to illustrate the performance of the proposed method, it is first tested using two established numerical examples and compared with the results obtained from alternative methods. A third example on energy dependency among 23 European Union (EU) member countries is further used to validate and describe the efficacy of our approach under asymmetric fuzzy numbers.

Generation of Pareto optimal solutions using generalized DEA and PSO

Meta-heuristic methods such as particle swarm optimization and genetic algorithms have been applied in solving multi-objective optimization problems, and have been observed to be useful for generating a good approximation of Pareto optimal solutions. This paper suggests a multi-objective particle swarm optimization (MOPSO) utilizing generalized data envelopment analysis (GDEA) in order to decide adaptively parameters of MOPSO as well as to improve the convergence and the diversity in the search of solutions. In addition, the effectiveness of the proposed method using GDEA will be investigated by comparison with conventional methods through several numerical examples.

Estimating Energy Consumption of Transport Modes in China Using DEA

The rapid growth of transport requirements in China will incur increasing transport energy demands and associated environmental pressures. In this paper, we employ a generalized data envelopment analysis (DEA) to evaluate the relative energy efficiency of rail, road, aviation and water transport from 1971 to 2011 by considering the energy input and passenger-kilometers (PKM) and freight ton-kilometers (TKM) outputs. The results show that the optimal energy efficiencies observed in 2011 are for rail and water transport, with the opposite observed for the energy efficiencies of aviation and road transport. In addition, we extend the DEA model to estimate future transport energy consumption in China. If each transport mode in 2020 is optimized throughout the observed period, the national transport energy consumption in 2020 will reach 497,701 kilotons coal equivalent (ktce), whereas the annual growth rate from 2011 to 2020 will be 5.7%. Assuming that efficiency improvements occur in this period, the estimated national transport energy consumption in 2020 will be 443,126 ktce, whereas the annual growth rate from 2011 to 2020 will be 4.4%, which is still higher than that of the national total energy consumption (3.8%).

A Computation Methods for Analysis on the Effect of Stock Market and its Application

As a barometer of the macroeconomic of a country and an important part of the capital market, stock market has attracted increasing and highlighted attention. As is well-known, Chinese stock market is known as 'policy market', however, the issue about whether the stock market is really influenced by these policies is always an important and hot topic. In this paper, by using generalized data envelopment analysis, an analysis on the effect of the new policies carried out in May 2012 is provided based on closing price, Tobin Q, circulation market value, turnover rate, and return on assets. Based on the above results, it can show the effect of stock market policies on Chinese Economy.

Utilizing expected improvement and generalized data envelopment analysis in multi-objective genetic algorithms

Meta-heuristic methods such as genetic algorithms (GA) and particle swarm optimization (PSO) have been extended to multi-objective optimization problems, and have been observed to be useful for finding good approximate Pareto optimal solutions. In order to improve the convergence and the diversity in the search of solutions using meta-heuristic methods, this paper suggests a new method to make offspring by utilizing the expected improvement (EI) and generalized data envelopment analysis (GDEA). In addition, the effectiveness of the proposed method will be investigated through several numerical examples in comparison with the conventional multi-objective GA and PSO methods.

Preference Elicitation in Generalized Data Envelopment Analysis: In Search of a New Energy Balance in Japan

The recent dramatic change in energy supply in Japan has prompted a search for a new energy-environment-economic efficiency policy, in which a compromise has to be found between a sufficient supply of energy resources, the development of low carbon emission technology, and a continuation of economic growth. The prefectures in Japan – 46 in total (excluding Tokyo) – are regarded as the institutional agents or decision-making units (DMUs) which are responsible for the design of a new sustainable energy balance in these regions. The main challenge is now to design an efficient energy-environment-economic system. The present paper aims to develop a balanced decision-support tool for achieving an efficient energy supply in all Japanese prefectures. To that end, a new variant of Data Envelopment Analysis (DEA) is presented, which is characterized by two integrated features: (i) the use of a general Euclidean Distance Method (EDM) to achieve the most appropriate movement towards the efficiency frontier surface (in contrast to the standard radial movement, leading to a uniform proportional input reduction – or uniform proportional output increase); (ii) the incorporation of preference-based (PB) adjustments in efficiency strategies regarding the input reduction allocation – or the output increase allocation – of DMUs in order to balance rigorous efficiency decisions with political priorities at the regional level. This paper illustrates this new methodology by means of an application to prefectural energy efficiency strategies in Japan.

期待改善量と一般化包絡分析法を用いたパレート最適解の生成法

期待改善量と一般化包絡分析法を用いたパレート最適解の生成法

Temporal and Spatial Analysis of Integrated Energy and Environment Efficiency in China Based on a Green GDP Index

China is experiencing a high speed economic development which may exert great pressure on the environment and energy systems. To measure the environmental and energy performance during the economic development process, this paper selected 30 provinces, cities or autonomous regions as the decision making unit (DMU), and proposed a Green GDP index (GGI) in view of energy intensity and pollution intensity using the generalized Data Envelopment Analysis (DEA) method, and the developing trends of integrated energy and environment efficiency of DMUs from 2006 to 2010 are also demonstrated by the Malmquist index. Results show that the integrated energy and environment efficiency varies for each DMU. GGI were both 1 in Beijing and Shanghai. GGI values for the developed cities in Eastern China, such as Guangdong, Fujian, Zhejiang, Tianjin, Jiangsu, and Hainan, ranked high, while those in the Northeast and Middle China remained relatively low. Moreover, there is a positive relationship between the GGI and per capita GDP with a correlation coefficient of 0.75. Increases in GGI are also observed in the results, representing great achievements are acquired in energy conservation and emission reduction. However, the GGIs do not converge to the green frontier across the provinces.

Predicting Japanese bank stock performance with a composite relative efficiency metric: A new investment tool

The paper's main objective is to predict bank stock performance one year ahead with a composite efficiency metric from relative contextual financial analysis. We bring together financial ratios, generalized data envelopment analysis and simulated annealing to rank Japanese banks on stock performance predicted from relative efficiency scores. An application of this ranking in a profitable investment strategy by designating long and short portfolios underscores the potential commercial value of the method. The method can also be used to monitor the effectiveness of ratios in forecasting stock performance and it is conducive to selecting predictive ratios when markets are changing rapidly.

A bi-objective generalized data envelopment analysis model and point-to-set mapping projection

This work introduces a bi-objective generalized data envelopment analysis (Bi-GDEA) model and defines its efficiency. We show the equivalence between the Bi-GDEA efficiency and the non-dominated solutions of the multi-objective programming problem defined on the production possibility set (PPS) and discuss the returns to scale under the Bi-GDEA model. The most essential contribution is that we further define a point-to-set mapping and the mapping projection of a decision making unit (DMU) on the frontier of the PPS under the Bi-GDEA model. We give an effective approach for the construction of the point-to-set-mapping projection which distinguishes our model from other non-radial models for simultaneously considering input and output. The Bi-GDEA model represents decision makers’ specific preference on input and output and the point-to-set mapping projection provides decision makers with more possibility to determine different input and output alternatives when considering efficiency improvement. Numerical examples are employed for the illustration of the procedure of point-to-set mapping.