Hye-Jin Kim
Yong-Hyuk Kim
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.
- Hye-Jin Kim, Junghwan Lee, Jong-Hwui Yun, and Yong-Hyuk Kim. 2019. Optimal equipment assignment for oil spill response using a genetic algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference Companion. ACM, Prague Czech Republic, 375--376.Google Scholar
Digital Library
- Jong-Hwui Yun, Dong-O Jo, Seunggi Guk, Yeongro Choi, Wondon Kim, Gyeong-Woo Jo, Dong-Hyeon Choi, Sang-Goo Kim, Jung-Hwan Moon, Ha-Yong Jang, Yeong-Nam Park, Eunmi Guk, and Eunbi Park. 2009. A Study on Practical Strategies for Estimating the National Control Ability of Oil Spill Control. Korea Maritime and Ocean University Technical Report. Korea Coast Guard.Google Scholar
Index Terms
- A surrogate model using deep neural networks for optimal oil skimmer assignment
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