Evolutionary Approach to Optimal Oil Skimmer Assignment for Oil Spill Response: A Case Study

We propose a genetic algorithm for optimizing oil skimmer assignments, introducing a tailored repair operation for constrained assignments. Methods essentially involve simulation-based evaluation to ensure adherence to South Korea’s regulations. Results show that the optimized assignments, compared...

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Bibliographic Details
Published inBiomimetics (Basel, Switzerland) Vol. 9; no. 6; p. 330
Main Authors Kim, Yong-Hyuk, Kim, Hye-Jin, Cho, Dong-Hee, Yoon, Yourim
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 30.05.2024
MDPI
Subjects
Algorithms
Case studies
Disasters
genetic algorithm
Linear programming
Neural networks
Oil recovery
oil skimmer assignment
Oil spills
Optimization
resource allocation
Simulation
surrogate model
Work capacity
Working hours
South Korea
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Summary:We propose a genetic algorithm for optimizing oil skimmer assignments, introducing a tailored repair operation for constrained assignments. Methods essentially involve simulation-based evaluation to ensure adherence to South Korea’s regulations. Results show that the optimized assignments, compared to current ones, reduced work time on average and led to a significant reduction in total skimmer capacity. Additionally, we present a deep neural network-based surrogate model, greatly enhancing efficiency compared to simulation-based optimization. Addressing inefficiencies in mobilizing locations that store oil skimmers, further optimization aimed to minimize mobilized locations and was validated through scenario-based simulations resembling actual situations. Based on major oil spills in South Korea, this strategy significantly reduced work time and required locations. These findings demonstrate the effectiveness of the proposed genetic algorithm and mobilized location minimization strategy in enhancing oil spill response operations.
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ISSN:2313-7673
2313-7673
DOI:10.3390/biomimetics9060330