What is he best known for?
The fields of study he is best known for:
- Algorithm
- Mathematical optimization
- Combinatorics
Martin Skutella spends much of his time researching Mathematical optimization, Approximation algorithm, Scheduling, Time complexity and Flow network.
His work carried out in the field of Mathematical optimization brings together such families of science as Algorithm and Job shop scheduling, Flow shop scheduling.
Martin Skutella has researched Approximation algorithm in several fields, including Graph theory and Randomized algorithm.
His Scheduling study integrates concerns from other disciplines, such as Efficient algorithm, Computation and Combinatorics.
His Time complexity research includes elements of Computational complexity theory, Discrete time and continuous time, Precedence diagram method and Transitive relation.
His Flow network study incorporates themes from Flow and Hardness of approximation.
His most cited work include:
- Approximation schemes for minimizing average weighted completion time with release dates (175 citations)
- An Introduction to Network Flows over Time (169 citations)
- Cooperative facility location games (142 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Mathematical optimization, Approximation algorithm, Algorithm, Time complexity and Flow network.
His Mathematical optimization research incorporates elements of Flow, Scheduling, Job shop scheduling and Special case.
The study incorporates disciplines such as Discrete mathematics, Linear programming relaxation and Completion time in addition to Approximation algorithm.
The concepts of his Algorithm study are interwoven with issues in Network planning and design, Dijkstra's algorithm and Telecommunications network.
His Time complexity study combines topics in areas such as Computational complexity theory and Discrete time and continuous time.
The Flow network study combines topics in areas such as Maximum flow problem, Simple and Time horizon.
He most often published in these fields:
- Mathematical optimization (50.25%)
- Approximation algorithm (27.64%)
- Algorithm (19.10%)
What were the highlights of his more recent work (between 2015-2021)?
- Mathematical optimization (50.25%)
- Combinatorics (16.58%)
- Scheduling (16.08%)
In recent papers he was focusing on the following fields of study:
Mathematical optimization, Combinatorics, Scheduling, Theoretical computer science and Combinatorial optimization are his primary areas of study.
His work deals with themes such as Time complexity and Job shop scheduling, which intersect with Mathematical optimization.
As a part of the same scientific family, Martin Skutella mostly works in the field of Job shop scheduling, focusing on Euler–Mascheroni constant and, on occasion, Approximation algorithm and Linear programming relaxation.
His work on Randomized rounding as part of his general Approximation algorithm study is frequently connected to Monotone polygon, thereby bridging the divide between different branches of science.
In the subject of general Combinatorics, his work in Graph is often linked to Matroid intersection, thereby combining diverse domains of study.
In the field of Combinatorial optimization, his study on Weapon target assignment problem overlaps with subjects such as Algorithmic game theory.
Between 2015 and 2021, his most popular works were:
- Robust Polynomial-Time Approximation Schemes for Parallel Machine Scheduling with Job Arrivals and Departures (21 citations)
- Unrelated Machine Scheduling with Stochastic Processing Times (20 citations)
- The Power of Recourse for Online MST and TSP (19 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Mathematical optimization
- Combinatorics
Martin Skutella mainly investigates Mathematical optimization, Online algorithm, Travelling salesman problem, Flow and Job shop scheduling.
His research links Queueing theory with Mathematical optimization.
His research investigates the link between Travelling salesman problem and topics such as Range that cross with problems in Current and Algorithm.
His studies in Flow integrate themes in fields like Network planning and design, Robust optimization, Path and Efficient algorithm.
His studies deal with areas such as Time complexity and Bounded function as well as Job shop scheduling.
His biological study spans a wide range of topics, including Single-machine scheduling, Approximation algorithm, Performance guarantee, Completion time and Linear programming relaxation.
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.
