L
Liang Liang
Researcher at Hefei University of Technology
Publications - 69
Citations - 2603
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.
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Variable selection in data envelopment analysis via Akaike’s information criteria
TL;DR: This study proposes an alternative approach to screen out proper input and output variables set for evaluation via Akaike’s information criteria (AIC) rule, which focuses on assessing the importance of subset of original variables rather than testing the marginal role of variables one by one in many other methods.
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Determining common weights in data envelopment analysis based on the satisfaction degree
TL;DR: A common-weight evaluation approach, which contains a max–min model and two algorithms, is proposed based on the satisfaction degrees of the decision-making units (DMUs), which makes the evaluation results more satisfied and acceptable by all the DMUs.
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Closest target for the orientation-free context-dependent DEA under variable returns to scale
TL;DR: The concept of closest target is introduced to determine the attractiveness and progress for each DMU, which can further improve DMUs’ performance with less wastes in inputs or underproduction in outputs.
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Carbon emission allocation in China based on gradually efficiency improvement and emission reduction planning principle
TL;DR: This study proposed an alternative approach based on gradually efficiency improvement planning and emission reduction planning principles for carbon emission allocation in China, and some realistic conclusions are obtained to guide in reality.
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A Shapley value index on the importance of variables in DEA models
Yongjun Li,Liang Liang +1 more
TL;DR: An efficiency change ratio (ECR) is defined to calculate the marginal impact of each variable on the efficiency evaluation, which compares the efficiency scores of the two radial DEA models differing in whether the given variable is included.