What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Mathematical optimization
His main research concerns Mathematical optimization, Remanufacturing, Analytic hierarchy process, Robustness and Multiple-criteria decision analysis.
His Mathematical optimization study combines topics in areas such as Stochastic simulation, Petri net and Job shop scheduling.
His Job shop scheduling research focuses on subjects like Algorithm, which are linked to Pareto principle.
His Remanufacturing study is concerned with the field of Manufacturing engineering as a whole.
His Robustness study combines topics from a wide range of disciplines, such as Engineering drawing, TOPSIS, Sensitivity, Decision support system and Ranking.
His work blends Multiple-criteria decision analysis and Ideal solution studies together.
His most cited work include:
- Green decoration materials selection under interior environment characteristics: A grey-correlation based hybrid MCDM method (113 citations)
- Green decoration materials selection under interior environment characteristics: A grey-correlation based hybrid MCDM method (113 citations)
- Disassembly Sequence Planning Considering Fuzzy Component Quality and Varying Operational Cost (108 citations)
What are the main themes of his work throughout his whole career to date?
Guangdong Tian mostly deals with Mathematical optimization, Process, Fuzzy logic, Operations research and Genetic algorithm.
Guangdong Tian has included themes like Remanufacturing and Job shop scheduling in his Mathematical optimization study.
His Remanufacturing research is multidisciplinary, incorporating elements of Multi criteria decision and Workstation.
His study looks at the relationship between Process and topics such as Reliability, which overlap with Product.
His research in Fuzzy logic intersects with topics in Multiple-criteria decision analysis, Selection and Robustness.
Guangdong Tian has researched Multiple-criteria decision analysis in several fields, including Ranking, Preference and Sensitivity.
He most often published in these fields:
- Mathematical optimization (48.76%)
- Process (19.83%)
- Fuzzy logic (25.62%)
What were the highlights of his more recent work (between 2019-2021)?
- Mathematical optimization (48.76%)
- Fuzzy logic (25.62%)
- Materials science (5.79%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Mathematical optimization, Fuzzy logic, Materials science, Operations research and Automotive engineering.
His studies deal with areas such as Preference, Remanufacturing and Process as well as Mathematical optimization.
His Remanufacturing study incorporates themes from Crossover, Machine tool and Reverse logistics.
His research integrates issues of Decision support system, Tuple, Regret and Interval in his study of Fuzzy logic.
The Operations research study combines topics in areas such as Fuzzy decision, Petri net and Interval valued.
His biological study spans a wide range of topics, including Choquet integral and Robustness.
Between 2019 and 2021, his most popular works were:
- An adaptive Lagrangian relaxation-based algorithm for a coordinated water supply and wastewater collection network design problem (44 citations)
- Evaluation of ship’s green degree using a novel hybrid approach combining group fuzzy entropy and cloud technique for the order of preference by similarity to the ideal solution theory (29 citations)
- Fuzzy Grey Choquet Integral for Evaluation of Multicriteria Decision Making Problems With Interactive and Qualitative Indices (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Mathematical optimization
Guangdong Tian mainly investigates Mathematical optimization, Cloud computing, Robustness, Preference and Multiple-criteria decision analysis.
Guangdong Tian specializes in Mathematical optimization, namely Genetic algorithm.
He incorporates a variety of subjects into his writings, including Cloud computing, Operations research, Risk assessment, Interval valued, Petri net and Fire accident.
His Robustness research includes themes of Multibody system, Grey relational analysis, Rail transportation and Vehicle dynamics.
Throughout his Preference studies, he incorporates elements of other sciences such as Rank, Order, Matrix and Consistency.
His Multiple-criteria decision analysis research is multidisciplinary, incorporating perspectives in Complex system and Fuzzy logic.
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