What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Mathematical optimization
Mathematical optimization, Simulated annealing, Artificial intelligence, Scheduling and Job shop scheduling are his primary areas of study.
Shih-Wei Lin has researched Mathematical optimization in several fields, including Vehicle routing problem and Benchmark.
His Simulated annealing study is concerned with the larger field of Algorithm.
His work in Artificial intelligence covers topics such as Pattern recognition which are related to areas like Particle swarm optimization and Computer vision.
His Scheduling research is multidisciplinary, relying on both Artificial immune system and Artificial immune system algorithm.
Shih-Wei Lin interconnects Data mining and Support vector machine in the investigation of issues within Feature selection.
His most cited work include:
- Particle swarm optimization for parameter determination and feature selection of support vector machines (629 citations)
- Parameter determination of support vector machine and feature selection using simulated annealing approach (245 citations)
- A simulated annealing heuristic for the capacitated location routing problem (198 citations)
What are the main themes of his work throughout his whole career to date?
Shih-Wei Lin spends much of his time researching Mathematical optimization, Job shop scheduling, Simulated annealing, Scheduling and Benchmark.
As part of one scientific family, Shih-Wei Lin deals mainly with the area of Mathematical optimization, narrowing it down to issues related to the Tardiness, and often Due date.
His Job shop scheduling research is multidisciplinary, incorporating perspectives in Algorithm, Integer programming and Problem set.
His work on Adaptive simulated annealing as part of general Simulated annealing research is frequently linked to Schedule, thereby connecting diverse disciplines of science.
In general Scheduling study, his work on Fair-share scheduling, Single-machine scheduling and Sequence-dependent setup often relates to the realm of Multiprocessing, thereby connecting several areas of interest.
His Benchmark research incorporates themes from Orienteering, Heuristic and Operations research.
He most often published in these fields:
- Mathematical optimization (89.02%)
- Job shop scheduling (61.59%)
- Simulated annealing (50.61%)
What were the highlights of his more recent work (between 2018-2021)?
- Job shop scheduling (61.59%)
- Mathematical optimization (89.02%)
- Benchmark (34.15%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Job shop scheduling, Mathematical optimization, Benchmark, Simulated annealing and Scheduling.
Many of his studies on Mathematical optimization involve topics that are commonly interrelated, such as Machine scheduling.
His biological study spans a wide range of topics, including Algorithm and Problem set.
His Simulated annealing study incorporates themes from Range, Location routing, Hybrid algorithm and Reduction.
His Scheduling research includes themes of Meta heuristic, Scheduling and Job shop.
The concepts of his Scheduling study are interwoven with issues in Local search and Heuristics.
Between 2018 and 2021, his most popular works were:
- Team orienteering problem with time windows and time-dependent scores (10 citations)
- Team orienteering problem with time windows and time-dependent scores (10 citations)
- Team orienteering problem with time windows and time-dependent scores (10 citations)
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