What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Mathematical optimization
- Statistics
His primary areas of investigation include Mathematical optimization, Algorithm, Global optimization, Quadratic programming and Combinatorial optimization.
His Mathematical optimization study deals with Quadratic equation intersecting with Branch and bound.
His studies deal with areas such as Assignment problem, Job shop scheduling and Electroencephalography as well as Algorithm.
In his study, which falls under the umbrella issue of Global optimization, Field is strongly linked to Nonlinear programming.
Panos M. Pardalos has researched Quadratic programming in several fields, including Discrete mathematics, Quadratic unconstrained binary optimization, Second-order cone programming and Combinatorics.
Panos M. Pardalos combines subjects such as Theoretical computer science, Approximation algorithm and Heuristics with his study of Combinatorial optimization.
His most cited work include:
- Handbook of global optimization (1240 citations)
- Introduction to global optimization (980 citations)
- Handbook of Combinatorial Optimization (830 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Mathematical optimization, Algorithm, Artificial intelligence, Global optimization and Optimization problem.
As part of his studies on Mathematical optimization, Panos M. Pardalos often connects relevant areas like Job shop scheduling.
His Job shop scheduling study is related to the wider topic of Scheduling.
His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition.
He most often published in these fields:
- Mathematical optimization (34.90%)
- Algorithm (14.12%)
- Artificial intelligence (10.28%)
What were the highlights of his more recent work (between 2016-2021)?
- Mathematical optimization (34.90%)
- Algorithm (14.12%)
- Job shop scheduling (4.27%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Mathematical optimization, Algorithm, Job shop scheduling, Optimization problem and Artificial intelligence.
His Mathematical optimization research is multidisciplinary, relying on both Vehicle routing problem and Theory of computation.
Many of his studies on Algorithm involve topics that are commonly interrelated, such as Function.
His research on Job shop scheduling often connects related topics like Scheduling.
His studies link Machine learning with Artificial intelligence.
Many of his studies involve connections with topics such as Learning effect and Scheduling.
Between 2016 and 2021, his most popular works were:
- Quantification of network structural dissimilarities (101 citations)
- A hybrid BA-VNS algorithm for coordinated serial-batching scheduling with deteriorating jobs, financial budget, and resource constraint in multiple manufacturers (62 citations)
- A hybrid BA-VNS algorithm for coordinated serial-batching scheduling with deteriorating jobs, financial budget, and resource constraint in multiple manufacturers (62 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Mathematical optimization
His primary scientific interests are in Mathematical optimization, Job shop scheduling, Algorithm, Scheduling and Scheduling.
His research brings together the fields of Supply chain and Mathematical optimization.
Within one scientific family, Panos M. Pardalos focuses on topics pertaining to Resource under Job shop scheduling, and may sometimes address concerns connected to Finance and Bat algorithm.
Panos M. Pardalos combines subjects such as Simplex, Support vector machine and Extension with his study of Algorithm.
His Heuristic research integrates issues from Local search and Heuristics.
In his research on the topic of Local search, Optimization problem is strongly related with Benchmark.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
