L
Liang Liang
Researcher at University of Science and Technology of China
Publications - 261
Citations - 10757
Liang Liang is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Data envelopment analysis & Supply chain. The author has an hindex of 51, co-authored 224 publications receiving 8736 citations. Previous affiliations of Liang Liang include Harbin Institute of Technology & Hefei University of Technology.
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Measuring performance of two-stage network structures by DEA: A review and future perspective
Wade D. Cook,Liang Liang,Joe Zhu +2 more
TL;DR: In this article, the authors present a review of data envelopment analysis (DEA) methods for peer decision-making units (DMUs) and show that all the existing approaches can be categorized as using either Stackelberg (leader-follower) or cooperative game concepts.
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DEA models for supply chain efficiency evaluation
TL;DR: It is shown that a supply chain can be deemed as efficient while its members may be inefficient in DEA-terms, and several DEA-based non-linear programs can be treated as parametric linear programming problems, and best solutions can be obtained via a heuristic technique.
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DEA Models for Two-Stage Processes: Game Approach and Efficiency Decomposition
Liang Liang,Wade D. Cook,Joe Zhu +2 more
TL;DR: In this paper, the authors examined and extended these models using game theory concepts and showed that the non-cooperative approach yields a unique efficiency decomposition under multiple intermediate measures, while the centralized approach is likely to yield multiple decompositions.
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Does environmental regulation affect energy efficiency in China's thermal power generation? Empirical evidence from a slacks-based DEA model
TL;DR: Wang et al. as mentioned in this paper investigated the relationship between fossil fuel consumption and the environmental regulation of China's thermal power generation and found that the environmental efficiency plays a significant role in affecting energy performance.
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The DEA Game Cross-Efficiency Model and Its Nash Equilibrium
TL;DR: The original DEA cross-efficiency concept is generalized to game cross efficiency, where each DMU is viewed as a player that seeks to maximize its own efficiency, under the condition that the cross efficiency of each of the other DMUs does not deteriorate.