What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Algorithm
- Artificial intelligence
Thibaut Vidal mainly investigates Mathematical optimization, Vehicle routing problem, Metaheuristic, Genetic algorithm and Benchmark.
His work on Heuristics and Local search as part of his general Mathematical optimization study is frequently connected to Quality, thereby bridging the divide between different branches of science.
His Vehicle routing problem study is focused on Routing in general.
His Genetic algorithm research is multidisciplinary, incorporating perspectives in Dimension and Neighbourhood.
His Benchmark study combines topics from a wide range of disciplines, such as Range, Heuristic, Data mining and Heuristic.
He interconnects Variety, Artificial intelligence, Development and Identification in the investigation of issues within Machine learning.
His most cited work include:
- A Hybrid Genetic Algorithm for Multidepot and Periodic Vehicle Routing Problems (344 citations)
- A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows (288 citations)
- Heuristics for multi-attribute vehicle routing problems: A survey and synthesis (265 citations)
What are the main themes of his work throughout his whole career to date?
Thibaut Vidal spends much of his time researching Mathematical optimization, Vehicle routing problem, Metaheuristic, Routing and Heuristics.
His Mathematical optimization study frequently draws connections between adjacent fields such as Benchmark.
His studies deal with areas such as Dynamic programming, Local search, Shortest path problem and Theoretical computer science as well as Vehicle routing problem.
The concepts of his Metaheuristic study are interwoven with issues in Variety, Branch and price and Combinatorial optimization.
His Routing research integrates issues from Integer programming and Operations research.
His Heuristics research includes elements of Development, Graph, Simulation and Identification.
He most often published in these fields:
- Mathematical optimization (74.77%)
- Vehicle routing problem (56.07%)
- Metaheuristic (45.79%)
What were the highlights of his more recent work (between 2019-2021)?
- Vehicle routing problem (56.07%)
- Mathematical optimization (74.77%)
- Routing (26.17%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Vehicle routing problem, Mathematical optimization, Routing, Artificial intelligence and Heuristic.
His Vehicle routing problem study combines topics in areas such as Transportation planning, Theoretical computer science, Combinatorial optimization and Metaheuristic.
His biological study spans a wide range of topics, including Assortativity and Benchmark.
His Arc routing study in the realm of Routing connects with subjects such as Flexibility.
In his study, Tree is strongly linked to Machine learning, which falls under the umbrella field of Artificial intelligence.
His research integrates issues of Column generation, Optimization algorithm, Heuristics and Heuristic in his study of Heuristic.
Between 2019 and 2021, his most popular works were:
- A concise guide to existing and emerging vehicle routing problem variants (47 citations)
- The vehicle routing problem with arrival time diversification on a multigraph (5 citations)
- Industrial and tramp ship routing problems: Closing the gap for real-scale instances (5 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Mathematical optimization
- Artificial intelligence
Thibaut Vidal focuses on Vehicle routing problem, Mathematical optimization, Supply chain, Combinatorial optimization and Data science.
Vehicle routing problem is the subject of his research, which falls under Routing.
His Mathematical optimization research incorporates themes from Multigraph and Benchmark.
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