| 边远海域救援船舶与直升机联合搜救优化 |
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| 引用本文： | 林婉妮, 王诺, 高忠印, 吴迪. 边远海域救援船舶与直升机联合搜救优化[J]. 交通运输工程学报, 2021, 21(2): 187-199. doi: 10.19818/j.cnki.1671-1637.2021.02.016 |
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| 作者姓名： | 林婉妮 王诺 高忠印 吴迪 |
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| 作者单位： | 1.军事科学院 系统工程研究院 后勤科学与技术研究所，北京 100166;;2.大连海事大学 交通运输工程学院，辽宁 大连 116026 |
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| 基金项目： | 国家自然科学基金项目42030409辽宁省社会科学基金青年项目L19CGJ001 |
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| 摘 要： | 以救援船舶行驶路线、释放救援直升机时刻与救援直升机搜索方案为优化内容，以搜救时间最短和发现概率最大为目标，建立了海空联合搜救双目标优化模型，并结合地理信息系统和智能算法设计了模型求解算法; 利用地理信息系统模拟了复杂海洋环境中风、浪因素影响下的救援船舶和遇险船舶运行状态，采用自适应混沌搜索替代随机搜索，改进了传统粒子群算法; 以从南海永兴岛出发前往边远海域执行搜救任务为算例，验证了搜救优化模型。研究结果表明：利用地理信息系统与智能算法结合的海空联合搜救方法得到的搜救行动总时间为4.4~16.9 h，发现概率可达45.12%~99.76%;与传统的粒子群算法相比，改进后的粒子群算法在发现概率分别为85.00%、90.00%与95.00%的情况下，搜救总时间分别减少1.5、1.3与1.1 h，减少幅度分别为18.07%、14.28%与10.57%，改进后的算法在计算速度、计算稳定性与结果优化方面均效果良好; 海空联合搜救方案优化与传统的多目标路径优化问题有所不同，需要建立特定的海空联合搜救模型，结合新的技术手段开展研究; 未来建议发展不同船型、机型参与的海空联合搜救优化方法，以适应不断提高边远海域搜救行动效率的发展要求。
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| 关 键 词： | 航运管理 海上搜救 海空联合 GIS 多目标优化 粒子群算法 |
| 收稿时间： | 2020-09-20 |
Associated searching and rescuing optimization of salvage vessels and helicopters in remote sea area |
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| LIN Wan-ni, WANG Nuo, GAO Zhong-yin, WU Di. Associated searching and rescuing optimization of salvage vessels and helicopters in remote sea area[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 187-199. doi: 10.19818/j.cnki.1671-1637.2021.02.016 |
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| Authors: | LIN Wan-ni WANG Nuo GAO Zhong-yin WU Di |
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| Affiliation: | 1. Institute of Logistics Science and Technology, Institute of Systems Engineering, Academy of Military Sciences, Beijing 100166, China;;2. College of Transportation Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China |
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| Abstract: | A bi-objective optimization model of air-sea associated searching and rescuing (SAR) was built, which took the time when the helicopter took off from the salvage vessel and the search plan of helicopter as optimization content, and aimed to minimize the SAR time and maximize the probability of discovery. An improved algorithm was designed based on a the geographic information system (GIS) and intelligent algorithms. The GIS was used to calculate the statuses of salvage vessels and vessels in distress under the influence of wind and wave factors in view of the changeable marine environment. The self-adaptive chaos search was used instead of random search to improve the particle swarm optimization algorithm. An example of the salvage vessel carrying a helicopter from Yongxing Island in the South China Sea to a remote sea area was used to verify the optimization model. Research results show that the total SAR time required for the SAR plan using GIS and intelligence algorithms is 4.4-16.9 h and the discovery probability is 45.12%-99.76%. Compared with the traditional particle swarm algorithm, the total SAR time of the improved particle swarm algorithm reduces by 1.5, 1.3, and 1.1 h, with a decrease rate of 18.07%, 14.28%, and 10.57% when the probability of discovery is 85.00%, 90.00%, and 95.00%, respectively. The improved algorithm shows better effect on calculation speed, calculation stability, and optimization result. The optimization of air-sea associated SAR is different from the traditional multi-objective routing optimization problem, and a new model that combines the improved algorithm is needed. To improve the efficiency of SAR in remote sea areas, it is suggested to further develop the optimization method used for air-sea associated SAR for different types of salvage vessels and helicopters. 6 tabs, 10 figs, 32 refs. |
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| Keywords: | marine management maritime searching and rescuing air-sea association GIS multi-objective optimization particle swarm algorithm |
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