Volume 21 Issue 2
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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
Citation: 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
shu

Associated searching and rescuing optimization of salvage vessels and helicopters in remote sea area

doi: 10.19818/j.cnki.1671-1637.2021.02.016
  • 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

Funds:

National Natural Science Foundation of China 42030409

Social Science Foundation Youth Project of Liaoning Province L19CGJ001

More Information
  • Author Bio:

    LIN Wan-ni(1991-), female, engineer, PhD, wn_lin@126.com

  • Corresponding author: WU Di(1989-), male, assistant professor, PhD, wudidlmu@163.com
  • Received Date: 2020-09-20
  • Publish Date: 2021-04-01
    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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