What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Statistics
- Scheduling
Gur Mosheiov mainly focuses on Mathematical optimization, Scheduling, Job shop scheduling, Heuristics and Time complexity.
His study in the field of Scheduling and Assignment problem also crosses realms of Upper and lower bounds, Sequence and Schedule.
His study on Tardiness and Fair-share scheduling is often connected to Project management as part of broader study in Scheduling.
His study looks at the relationship between Fair-share scheduling and fields such as Distributed computing, as well as how they intersect with chemical problems.
His Job shop scheduling research is multidisciplinary, relying on both Algorithm and Minification.
In his study, Optimal cost is inextricably linked to Asymptotically optimal algorithm, which falls within the broad field of Heuristics.
His most cited work include:
- Scheduling problems with a learning effect (300 citations)
- Scheduling with general job-dependent learning curves (269 citations)
- Scheduling jobs under simple linear deterioration (265 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Mathematical optimization, Scheduling, Job shop scheduling, Time complexity and Tardiness.
His study in Mathematical optimization is interdisciplinary in nature, drawing from both Single-machine scheduling and Flow shop scheduling.
As part of the same scientific family, Gur Mosheiov usually focuses on Scheduling, concentrating on Heuristics and intersecting with Heuristic.
His study looks at the relationship between Job shop scheduling and topics such as Job scheduler, which overlap with Distributed computing.
As part of his studies on Time complexity, Gur Mosheiov frequently links adjacent subjects like Operations research.
His research in Dynamic programming intersects with topics in Completion time, Scheduling and Theory of computation.
He most often published in these fields:
- Mathematical optimization (80.79%)
- Scheduling (67.55%)
- Job shop scheduling (37.75%)
What were the highlights of his more recent work (between 2017-2021)?
- Mathematical optimization (80.79%)
- Scheduling (67.55%)
- Dynamic programming (20.53%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Mathematical optimization, Scheduling, Dynamic programming, Job rejection and Job shop scheduling.
His work on Scheduling is typically connected to Upper and lower bounds as part of general Mathematical optimization study, connecting several disciplines of science.
In Scheduling, Gur Mosheiov works on issues like Theory of computation, which are connected to Tardiness.
His Dynamic programming course of study focuses on Unavailability and Heuristics.
Gur Mosheiov merges Job shop scheduling with Supply chain management in his research.
His work on Open-shop scheduling and Open shop as part of general Flow shop scheduling research is frequently linked to Window, bridging the gap between disciplines.
Between 2017 and 2021, his most popular works were:
- Scheduling on a proportionate flowshop to minimise total late work (14 citations)
- A note: minimizing total absolute deviation of job completion times on unrelated machines with general position-dependent processing times and job-rejection (13 citations)
- Minimizing total load on a proportionate flowshop with position-dependent processing times and job-rejection (13 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Mathematical optimization
- Algorithm
Gur Mosheiov focuses on Mathematical optimization, Job shop scheduling, Job rejection, Scheduling and Flow shop scheduling.
When carried out as part of a general Mathematical optimization research project, his work on Dynamic programming and Scheduling is frequently linked to work in Position dependent and Absolute deviation, therefore connecting diverse disciplines of study.
His study in Dynamic programming intersects with areas of studies such as Upper and lower bounds, Supply chain management and Learning effect.
His Scheduling study frequently draws connections to adjacent fields such as Combinatorial optimization.
Position dependent is integrated with Extension, Process, Assignment problem and Subroutine in his study.
His work on Open shop and Open-shop scheduling as part of general Flow shop scheduling study is frequently linked to Window, therefore connecting diverse disciplines of science.
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