What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Artificial intelligence
- Algorithm
Kuo-Ching Ying mainly investigates Mathematical optimization, Simulated annealing, Job shop scheduling, Iterated greedy and Greedy algorithm.
In general Mathematical optimization, his work in Scheduling and Heuristic is often linked to Sequence-dependent setup linking many areas of study.
His Simulated annealing research includes elements of Genetic algorithm, Tabu search and Metaheuristic.
Kuo-Ching Ying is interested in Flow shop scheduling, which is a field of Job shop scheduling.
As a member of one scientific family, he mostly works in the field of Algorithm, focusing on Anomaly-based intrusion detection system and, on occasion, Support vector machine and Feature selection.
His study looks at the relationship between Search algorithm and topics such as Artificial intelligence, which overlap with Particle swarm optimization.
His most cited work include:
- Particle swarm optimization for parameter determination and feature selection of support vector machines (629 citations)
- An ant colony system for permutation flow-shop sequencing (159 citations)
- An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection (152 citations)
What are the main themes of his work throughout his whole career to date?
Kuo-Ching Ying mostly deals with Mathematical optimization, Job shop scheduling, Simulated annealing, Scheduling and Metaheuristic.
His Mathematical optimization research integrates issues from Algorithm and Benchmark.
His work on Flow shop scheduling as part of general Job shop scheduling study is frequently linked to Problem set, Heuristics, Manufacturing cell and Ant colony, therefore connecting diverse disciplines of science.
His studies in Simulated annealing integrate themes in fields like Genetic algorithm and Tabu search.
Distributed manufacturing is closely connected to Industrial engineering in his research, which is encompassed under the umbrella topic of Scheduling.
His biological study spans a wide range of topics, including Machine learning and Pattern recognition.
He most often published in these fields:
- Mathematical optimization (73.64%)
- Job shop scheduling (51.82%)
- Simulated annealing (31.82%)
What were the highlights of his more recent work (between 2018-2021)?
- Job shop scheduling (51.82%)
- Mathematical optimization (73.64%)
- Metaheuristic (22.73%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Job shop scheduling, Mathematical optimization, Metaheuristic, Scheduling and Simulated annealing.
Kuo-Ching Ying integrates many fields, such as Job shop scheduling, Benchmark and Approximation algorithm, in his works.
His study in the field of Genetic algorithm and Integer programming is also linked to topics like Machine scheduling.
His Metaheuristic research integrates issues from Robot and Local search.
His Scheduling study combines topics from a wide range of disciplines, such as Analytics and Industrial engineering.
The various areas that Kuo-Ching Ying examines in his Simulated annealing study include Advanced manufacturing and Meta heuristic.
Between 2018 and 2021, his most popular works were:
- Minimising makespan in distributed mixed no-idle flowshops (21 citations)
- Minimizing makespan in mixed no-wait flowshops with sequence-dependent setup times (8 citations)
- Multi-temperature simulated annealing for optimizing mixed-blocking permutation flowshop scheduling problems (7 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Mathematical optimization
- Algorithm
Kuo-Ching Ying mainly focuses on Job shop scheduling, Mathematical optimization, Metaheuristic, Scheduling and Simulation.
Among his research on Job shop scheduling, you can see a combination of other fields of science like Manufacturing cell and Optimization problem.
Mathematical optimization is often connected to Benchmark in his work.
His Metaheuristic research incorporates elements of Genetic algorithm and Local search.
His Scheduling study incorporates themes from Industrial engineering, Simulated annealing, Distributed manufacturing and Advanced manufacturing.
As part of his studies on Simulation, Kuo-Ching Ying often connects relevant subjects like Beam search.
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