What is he best known for?
The fields of study he is best known for:
- Statistics
- Computer network
- Mathematical optimization
His primary areas of study are Mathematical optimization, Lagrangian relaxation, Simulation, Algorithm and Mobile computing.
The Mathematical optimization study combines topics in areas such as Reduction, Mathematical model and Scheduling.
His studies in Lagrangian relaxation integrate themes in fields like Lagrange multiplier, Focus, Knapsack problem and Flow network.
His studies deal with areas such as Synthetic data, Nonlinear mixed integer programming and Traffic flow as well as Simulation.
His study in the field of Branch and bound is also linked to topics like Descent direction.
His Mobile computing study integrates concerns from other disciplines, such as Wireless, Assisted GPS and Key.
His most cited work include:
- Single-Track Train Timetabling with Guaranteed Optimality: Branch-and-Bound Algorithms with Enhanced Lower Bounds (213 citations)
- Optimizing urban rail timetable under time-dependent demand and oversaturated conditions (200 citations)
- Train scheduling for minimizing passenger waiting time with time-dependent demand and skip-stop patterns: Nonlinear integer programming models with linear constraints (166 citations)
What are the main themes of his work throughout his whole career to date?
Xuesong Zhou mainly focuses on Mathematical optimization, Lagrangian relaxation, Transport engineering, Simulation and Operations research.
His study on Dynamic programming, Flow network and Integer programming is often connected to Schedule as part of broader study in Mathematical optimization.
His Integer programming research is multidisciplinary, incorporating elements of Linear programming, Network planning and design and Heuristic.
His Lagrangian relaxation research includes themes of Scheduling, Lagrange multiplier, Vehicle routing problem and Network model.
His research investigates the link between Simulation and topics such as Traffic simulation that cross with problems in Traffic generation model, Queue and Signal timing.
His biological study spans a wide range of topics, including Kalman filter, Transportation planning, Intelligent transportation system and Representation.
He most often published in these fields:
- Mathematical optimization (37.29%)
- Lagrangian relaxation (19.77%)
- Transport engineering (18.08%)
What were the highlights of his more recent work (between 2019-2021)?
- Traffic flow (10.73%)
- Deep learning (2.26%)
- Artificial intelligence (2.82%)
In recent papers he was focusing on the following fields of study:
Xuesong Zhou spends much of his time researching Traffic flow, Deep learning, Artificial intelligence, Mathematical optimization and Flow network.
His study focuses on the intersection of Traffic flow and fields such as Trajectory with connections in the field of Real-time computing, Scheduling, Queueing theory and Sensitivity.
His studies in Artificial intelligence integrate themes in fields like Machine learning, Econometric model, Service and Traffic congestion.
In general Mathematical optimization study, his work on Integer programming often relates to the realm of Shortest path problem, thereby connecting several areas of interest.
Xuesong Zhou combines subjects such as Green logistics, Genetic algorithm, Shared resource, Lagrangian relaxation and Operations research with his study of Flow network.
His Lagrangian relaxation research includes elements of TRIPS architecture and Network topology.
Between 2019 and 2021, his most popular works were:
- Green logistics location-routing problem with eco-packages (23 citations)
- Trajectory data-based traffic flow studies: A revisit (12 citations)
- Yard crane and AGV scheduling in automated container terminal: A multi-robot task allocation framework (8 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Computer network
- Artificial intelligence
His scientific interests lie mostly in Mathematical optimization, Flow network, Big data, Trajectory and Data collection.
Borrowing concepts from Flow balance, he weaves in ideas under Mathematical optimization.
His work carried out in the field of Flow network brings together such families of science as Sorting, Lagrangian relaxation, Shared resource and Green logistics.
His Big data research incorporates elements of Real-time computing and Traffic flow.
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