AI Article Synopsis
- The study introduces a genetic algorithm to optimize the assignment of oil skimmers, incorporating a special repair operation for constrained tasks while meeting South Korean regulations.
- It shows that optimized assignments lead to reduced work time and lower total skimmer capacity compared to current practices.
- Additionally, a deep neural network-based surrogate model improves efficiency, validating the strategy through simulations that mirror actual oil spill scenarios, significantly cutting down on both mobilization time and required locations.
Article Abstract
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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11202193 | PMC |
| http://dx.doi.org/10.3390/biomimetics9060330 | DOI Listing |
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