Evolutionary Approach to Optimal Oil Skimmer Assignment for Oil Spill Response: A Case Studyopen access
- Authors
- Kim, Yong-Hyuk; Kim, Hye-Jin; Cho, Dong-Hee; Yoon, Yourim
- Issue Date
- Jun-2024
- Publisher
- MDPI
- Keywords
- oil skimmer assignment; genetic algorithm; surrogate model; resource allocation
- Citation
- BIOMIMETICS, v.9, no.6
- Journal Title
- BIOMIMETICS
- Volume
- 9
- Number
- 6
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/91966
- DOI
- 10.3390/biomimetics9060330
- ISSN
- 2313-7673
2313-7673
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.