Paper Info
Title
A surrogate model using deep neural networks for optimal oil skimmer assignment
Abstract
Optimization of equipment assignments for oil spill responses entails several real-world constraints. We proposes a surrogate model which utilizes a deep neural network for the optimization of oil-removal equipment assignments. The surrogate model was constructed by applying machine-learning to 20,000 assignment plan data, all of which satisfy various constraining conditions based on deep neural networks. Compared to the existing optimization model, the constructed model showed a 61% increase in efficiency.
Year
DOI
Venue
2020
10.1145/3377929.3398158
GECCO '20: Genetic and Evolutionary Computation Conference Cancún Mexico July, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-7127-8
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Hye-Jin Kim13917.46
Yong-Hyuk Kim235540.27