What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Algorithm
- Artificial intelligence
His scientific interests lie mostly in Mathematical optimization, Multi-objective optimization, Combinatorial optimization, Operations research and Metaheuristic.
His study connects Upper and lower bounds and Mathematical optimization.
Many of his research projects under Multi-objective optimization are closely connected to Context with Context, tying the diverse disciplines of science together.
His research integrates issues of Optimization problem, L-reduction and Heuristic in his study of Combinatorial optimization.
His work in the fields of Operations research, such as Multiple-criteria decision analysis, intersects with other areas such as Schedule.
Matthias Ehrgott has researched Management science in several fields, including Goal programming, Decision support system, Decision analysis, Evolutionary algorithm and Decision rule.
His most cited work include:
- Multiple criteria decision analysis: state of the art surveys (2881 citations)
- Multicriteria Optimization (2155 citations)
- A survey and annotated bibliography of multiobjective combinatorial optimization (573 citations)
What are the main themes of his work throughout his whole career to date?
Matthias Ehrgott spends much of his time researching Mathematical optimization, Multi-objective optimization, Combinatorial optimization, Optimization problem and Operations research.
His research on Mathematical optimization often connects related areas such as Algorithm.
Matthias Ehrgott usually deals with Multi-objective optimization and limits it to topics linked to Heuristics and Local search.
Matthias Ehrgott interconnects Assignment problem, Discrete mathematics and Heuristic in the investigation of issues within Combinatorial optimization.
His work on Multiple-criteria decision analysis as part of his general Operations research study is frequently connected to Plan, thereby bridging the divide between different branches of science.
His Integer programming study incorporates themes from Generalized assignment problem and Weapon target assignment problem.
He most often published in these fields:
- Mathematical optimization (69.74%)
- Multi-objective optimization (17.95%)
- Combinatorial optimization (16.41%)
What were the highlights of his more recent work (between 2015-2021)?
- Mathematical optimization (69.74%)
- Data envelopment analysis (6.15%)
- Linear programming (8.72%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Mathematical optimization, Data envelopment analysis, Linear programming, Algorithm and Multi-objective optimization.
Specifically, his work in Mathematical optimization is concerned with the study of Optimization problem.
His Data envelopment analysis research is multidisciplinary, relying on both Quality and Duality.
In his study, Iterative method, Monotonic function, Leverage and Robust optimization is inextricably linked to Uncertain data, which falls within the broad field of Linear programming.
His Algorithm research incorporates elements of Single objective and Variation.
His study in Multi-objective optimization is interdisciplinary in nature, drawing from both Pareto principle and Duality.
Between 2015 and 2021, his most popular works were:
- Multiple Criteria Decision Analysis (260 citations)
- Continuous Multiobjective Programming (22 citations)
- Uncertain Data Envelopment Analysis (19 citations)
In his most recent research, the most cited papers focused on:
- Mathematical optimization
- Algorithm
- Artificial intelligence
His primary scientific interests are in Mathematical optimization, Representation, Linear programming, Multiple-criteria decision analysis and Machine learning.
The various areas that Matthias Ehrgott examines in his Mathematical optimization study include Decision support system, Theory of computation and Computation.
His Representation research includes themes of Intersection, Decision problem, State and Bilevel optimization.
His studies deal with areas such as Data envelopment analysis, Boundary and Monotonic function as well as Linear programming.
His research in Multiple-criteria decision analysis intersects with topics in Evidential reasoning approach and Artificial intelligence.
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