What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- The Internet
His scientific interests lie mostly in Mathematical optimization, Fair-share scheduling, Rate-monotonic scheduling, Dynamic priority scheduling and Resource allocation.
His biological study deals with issues like Schedule, which deal with fields such as Simulated annealing and Schedule.
His Fair-share scheduling study often links to related topics such as Flow shop scheduling.
His study on Flow shop scheduling is covered under Scheduling.
His Resource allocation research is multidisciplinary, relying on both Human resource management system, Resource and Grid resource management.
His study looks at the relationship between Two-level scheduling and fields such as Job shop scheduling, as well as how they intersect with chemical problems.
His most cited work include:
- Scheduling Computer and Manufacturing Processes (519 citations)
- Grid resource management: state of the art and future trends (361 citations)
- Project scheduling : recent models, algorithms, and applications (257 citations)
What are the main themes of his work throughout his whole career to date?
Jan Węglarz mainly investigates Scheduling, Mathematical optimization, Job shop scheduling, Distributed computing and Scheduling.
His research in Scheduling tackles topics such as Parallel computing which are related to areas like Gang scheduling.
His study in Mathematical optimization is interdisciplinary in nature, drawing from both Schedule and Open-shop scheduling.
His research in Job shop scheduling intersects with topics in Processor scheduling, Minification, Time complexity, Schedule and Heuristic.
Jan Węglarz has researched Scheduling in several fields, including Resource allocation, Project management and Operations research.
His Flow shop scheduling study incorporates themes from Rate-monotonic scheduling and Industrial engineering.
He most often published in these fields:
- Scheduling (41.50%)
- Mathematical optimization (26.53%)
- Job shop scheduling (19.73%)
What were the highlights of his more recent work (between 2011-2021)?
- Scheduling (41.50%)
- Distributed computing (17.69%)
- Scheduling (17.01%)
In recent papers he was focusing on the following fields of study:
His main research concerns Scheduling, Distributed computing, Scheduling, Mathematical optimization and Job shop scheduling.
His Scheduling research incorporates themes from Schedule, Scalability and Parallel computing.
His Distributed computing research is multidisciplinary, incorporating elements of Workload, Many core, Server and Resource allocation.
Jan Węglarz combines subjects such as Automotive engineering and Operations research with his study of Scheduling.
His work on Mathematical optimization is being expanded to include thematically relevant topics such as Open-shop scheduling.
His study on Multiprocessor scheduling is often connected to Energy source and Qubit as part of broader study in Job shop scheduling.
Between 2011 and 2021, his most popular works were:
- Handbook on Scheduling: From Theory to Applications (183 citations)
- Practical power consumption estimation for real life HPC applications (51 citations)
- DCworms - A tool for simulation of energy efficiency in distributed computing infrastructures (36 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- The Internet
Jan Węglarz mainly focuses on Distributed computing, Scheduling, Schedule, Supercomputer and Workload.
The Distributed computing study combines topics in areas such as Mathematical optimization, Multiprocessor scheduling, Server and Job shop scheduling.
Jan Węglarz integrates Scheduling and Supply chain management in his research.
In his work, Scalability, Symmetric multiprocessor system and Multi-core processor is strongly intertwined with Heuristics, which is a subfield of Schedule.
His research integrates issues of Uncertainty quantification, Systems engineering and Python in his study of Supercomputer.
His Workload research is multidisciplinary, incorporating perspectives in Grid computing, Data structure and Reservation.
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