What is he best known for?
The fields of study he is best known for:
- Statistics
- Microeconomics
- Marketing
Liang Liang mainly focuses on Data envelopment analysis, Supply chain, Operations research, Microeconomics and Ranking.
His work on Cross efficiency as part of general Data envelopment analysis research is often related to Stage, thus linking different fields of science.
His Supply chain research integrates issues from Stackelberg competition, Theory of computation and Operations management.
Liang Liang has included themes like Iterative method, Valuation and Nash equilibrium in his Operations research study.
Liang Liang focuses mostly in the field of Microeconomics, narrowing it down to topics relating to Decentralised system and, in certain cases, Communication channel, Pareto principle and Supply chain efficiency.
His work in Ranking tackles topics such as Data mining which are related to areas like Drawback and Rank.
His most cited work include:
- DEA models for supply chain efficiency evaluation (325 citations)
- Measuring performance of two-stage network structures by DEA: A review and future perspective (318 citations)
- DEA Models for Two-Stage Processes: Game Approach and Efficiency Decomposition (307 citations)
What are the main themes of his work throughout his whole career to date?
Liang Liang spends much of his time researching Data envelopment analysis, Operations research, Supply chain, Mathematical optimization and Microeconomics.
Liang Liang conducted interdisciplinary study in his works that combined Data envelopment analysis and Stage.
His Operations research research incorporates themes from Competition, Data set, Operations management and Measure.
His study in Supply chain is interdisciplinary in nature, drawing from both Stackelberg competition, Profit and Industrial organization.
His Industrial organization study combines topics in areas such as Marketing and Game theory.
His Linear programming study in the realm of Mathematical optimization interacts with subjects such as Efficient frontier.
He most often published in these fields:
- Data envelopment analysis (45.38%)
- Operations research (27.73%)
- Supply chain (24.79%)
What were the highlights of his more recent work (between 2017-2021)?
- Data envelopment analysis (45.38%)
- Operations research (27.73%)
- Set (12.61%)
In recent papers he was focusing on the following fields of study:
Liang Liang mostly deals with Data envelopment analysis, Operations research, Set, Mathematical optimization and Fixed cost.
His research integrates issues of Incentive, Production and Environmental economics in his study of Data envelopment analysis.
As a part of the same scientific study, Liang Liang usually deals with the Production, concentrating on Empirical research and frequently concerns with Marketing and Information flow.
His Environmental economics study which covers Linkage that intersects with Inefficiency.
His studies in Operations research integrate themes in fields like Variable, Data set and Akaike information criterion.
His work carried out in the field of Mathematical optimization brings together such families of science as Profit and Process.
Between 2017 and 2021, his most popular works were:
- Allocating a fixed cost based on a DEA-game cross efficiency approach (51 citations)
- Allocating a fixed cost based on a DEA-game cross efficiency approach (51 citations)
- Policy implications for promoting the adoption of electric vehicles: Do consumer’s knowledge, perceived risk and financial incentive policy matter? (41 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Marketing
- Microeconomics
His primary areas of study are Data envelopment analysis, Fixed cost, Set, Mathematical optimization and Process.
His Data envelopment analysis research is multidisciplinary, incorporating elements of Inefficiency, Environmental economics and Linkage.
His biological study spans a wide range of topics, including Shapley value, Competition, Valuation and Characteristic function.
The Set study combines topics in areas such as Cooperative game theory and Operations research.
He interconnects Production, Empirical research and Revenue in the investigation of issues within Mathematical optimization.
His Process study often links to related topics such as Maximization.
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