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Liang Liang

Bio: Liang Liang is an academic researcher from Hefei University of Technology. The author has contributed to research in topics: Data envelopment analysis & Chemistry. The author has an hindex of 22, co-authored 62 publications receiving 1955 citations. Previous affiliations of Liang Liang include University of Science and Technology of China.


Papers
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Journal ArticleDOI
TL;DR: Two models are proposed to evaluate the performance of this type general two-stage network structures where all outputs of the first stage are the only inputs to the second stage, and a non-cooperative model, in which one of the stages is regarded as the leader and the other is the follower.
Abstract: This chapter discusses DEA modeling technique for a two-stage network process where the inputs of the second stage include both the outputs from the first stage and additional inputs to the second stage. Two models are proposed to evaluate the performance of this type two-stage network structures. One is a non-linear centralized model whose global optimal solutions can be estimated using a heuristic search procedure. The other is a non-cooperative model, in which one of the stages is regarded as the leader and the other is the follower. The newly developed models are illustrated with a case of regional R&D of China.

231 citations

Journal ArticleDOI
TL;DR: An integrated binary discriminant rule (IBDR) for corporate financial distress prediction is developed by interpreting and modifying the outputs of the SVM classifiers according to the result of logistic regression analysis.
Abstract: The support vector machine (SVM) has been applied to the problem of bankruptcy prediction, and proved to be superior to competing methods such as the neural network, the linear multiple discriminant approaches and logistic regression. However, the conventional SVM employs the structural risk minimization principle, thus empirical risk of misclassification may be high, especially when a point to be classified is close to the hyperplane. This paper develops an integrated binary discriminant rule (IBDR) for corporate financial distress prediction. The described approach decreases the empirical risk of SVM outputs by interpreting and modifying the outputs of the SVM classifiers according to the result of logistic regression analysis. That is, depending on the vector's relative distance from the hyperplane, if result of logistic regression supports the output of the SVM classifier with a high probability, then IBDR will accept the output of the SVM classifier; otherwise, IBDR will modify the output of the SVM classifier. Our experimentation results demonstrate that IBDR outperforms the conventional SVM.

205 citations

Journal ArticleDOI
TL;DR: A new approach of defining reference set that requires reference units operate in a similar environment on average is described, and a non-radial output-oriented DEA model is proposed to provide efficient inputs/outputs targets for DMU managers to improve DMUs' efficiencies.
Abstract: The objective of this paper is to estimate the ecological efficiency of paper mills along the Huai River in China. The main characteristic of the ecological efficiency evaluation problem is that an undesirable output of biochemical oxygen demand (BOD) and a non-discretionary input (BOD emission quota) should be considered simultaneously. By analyzing the impacts of the non-discretionary input on decision-making units’ (DMUs) desirable and undesirable outputs, a non-radial output-oriented DEA model is proposed. In the proposed model, we describe a new approach of defining reference set that requires reference units operate in a similar environment on average. We employ the model to provide efficient inputs/outputs targets for DMU managers to improve DMUs’ efficiencies. Based on the developed model, impacts of the non-discretionary input on DMUs’ returns are also analyzed. We illustrate the proposed model, using real data, for 32 paper mills along the Huai River in China.

183 citations

Journal ArticleDOI
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

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper extended the DEA model to consider two-sided nonhomogeneous problems, handling DMU sets that have non-homogeneity in both inputs and outputs, and the overall efficiency of 38 industrial sectors in China maintained a rising trend in five years.

120 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper 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.
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.

774 citations

Journal Article

663 citations

Journal ArticleDOI
TL;DR: This study is the first literature survey that focuses on DEA applications, covering DEA papers published in journals indexed by the Web of Science database from 1978 through August 2010, and suggests that the two-step contextual analysis and network DEA are the recent trends across applications.
Abstract: The literature of data envelopment analysis (DEA) encompasses many surveys, yet all either emphasize methodologies or do not make a distinction between methodological and application papers. This study is the first literature survey that focuses on DEA applications, covering DEA papers published in journals indexed by the Web of Science database from 1978 through August 2010. The results show that on the whole around two-thirds (63.6%) of DEA papers embed empirical data, while the remaining one-third are purely-methodological. Purely-methodological articles dominated the first 20 years of DEA development, but the accumulated number of application-embedded papers caught up to purely-methodological papers in 1999. Among the multifaceted applications, the top-five industries addressed are: banking, health care, agriculture and farm, transportation, and education. The applications that have the highest growth momentum recently are energy and environment as well as finance. In addition to the basic statistics, we uncover the development trajectory in each application area through the main path analysis. An observation from these works suggests that the two-step contextual analysis and network DEA are the recent trends across applications and that the two-step contextual analysis is the prevailing approach.

622 citations

Journal ArticleDOI
TL;DR: Cengage Learning, 2000. Brand New, Unread Copy in Perfect Condition as discussed by the authors. But they did not specify the exact condition of the book, only that it was in perfect condition.
Abstract: Cengage Learning, 2000. Book Condition: New. Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary:

554 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a dynamic DEA model involving network structure in each period within the framework of a slacks-based measure approach, and applied this model to a dataset of US electric utilities and compared the result with that of DSBM.
Abstract: We propose a dynamic DEA model involving network structure in each period within the framework of a slacks-based measure approach. We have previously published the network SBM (NSBM) and the dynamic SBM (DSBM) models separately. Hence, this article is a composite of these two models. Vertically, we deal with multiple divisions connected by links of network structure within each period and, horizontally, we combine the network structure by means of carry-over activities between two succeeding periods. This model can evaluate (1) the overall efficiency over the entire observed period, (2) dynamic change of period efficiency and (3) dynamic change of divisional efficiency. The model can be implemented in input-, output- or non-(both) oriented forms under the CRS or VRS assumptions on the production possibility set. Finally, we applied this model to a dataset of US electric utilities and compared the result with that of DSBM.

480 citations