What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Statistics
- Artificial intelligence
Andrew Lim mostly deals with Mathematical optimization, Heuristics, Operations research, Tabu search and Scheduling.
His study ties his expertise on Algorithm together with the subject of Mathematical optimization.
His Heuristics study which covers Container that intersects with Combinatorial optimization and Range.
His work carried out in the field of Operations research brings together such families of science as Linear programming and Neighborhood search.
His Tabu search research includes elements of Simulated annealing, Dynamic programming and Greedy algorithm.
His research investigates the connection between Scheduling and topics such as Genetic algorithm that intersect with issues in Flow shop scheduling and Open-shop scheduling.
His most cited work include:
- Nurse rostering problems––a bibliographic survey (411 citations)
- The berth planning problem11This research is part of a project the author has with the Port of Singapore Authority. The author was a senior technical consultant in that project. This research was supported in part by the NUS Research Front RP3972679. (257 citations)
- Reliable logistics networks design with facility disruptions (243 citations)
What are the main themes of his work throughout his whole career to date?
Andrew Lim spends much of his time researching Mathematical optimization, Algorithm, Heuristic, Heuristics and Tabu search.
The concepts of his Mathematical optimization study are interwoven with issues in Vehicle routing problem and Benchmark.
Vehicle routing problem is closely attributed to Local search in his work.
His studies in Heuristic integrate themes in fields like Container and Integer programming.
His Heuristics research is multidisciplinary, incorporating perspectives in Scheduling and Operations research.
He frequently studies issues relating to Greedy algorithm and Tabu search.
He most often published in these fields:
- Mathematical optimization (67.50%)
- Algorithm (17.40%)
- Heuristic (16.83%)
What were the highlights of his more recent work (between 2015-2021)?
- Mathematical optimization (67.50%)
- Vehicle routing problem (14.15%)
- Branch and price (4.21%)
In recent papers he was focusing on the following fields of study:
Mathematical optimization, Vehicle routing problem, Branch and price, Artificial intelligence and Heuristic are his primary areas of study.
His biological study spans a wide range of topics, including Routing, Container and Job shop scheduling.
His research in Routing intersects with topics in Dynamic programming and Heuristics.
His Vehicle routing problem research integrates issues from Algorithm, Fractional programming and Principal.
His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition.
His studies deal with areas such as Integer programming and Skyline as well as Heuristic.
Between 2015 and 2021, his most popular works were:
- Learning Robust Features using Deep Learning for Automatic Seizure Detection (126 citations)
- Machine Learning and Portfolio Optimization (51 citations)
- Robust empirical optimization is almost the same as mean–variance optimization (37 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Mathematical optimization
His primary areas of investigation include Mathematical optimization, Vehicle routing problem, Branch and price, Pickup and Algorithm.
He has included themes like Container and Job shop scheduling in his Mathematical optimization study.
Reinforcement learning, Limit and Selection is closely connected to Travelling salesman problem in his research, which is encompassed under the umbrella topic of Vehicle routing problem.
He has researched Branch and price in several fields, including Column generation, Preventive maintenance and Single-machine scheduling.
The various areas that he examines in his Algorithm study include Disjoint sets and Group.
His work deals with themes such as Stochastic process, Expected cost, Dynamic programming and Large neighborhood search, which intersect with Heuristics.
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