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International Journal of Mathematical, Engineering and Management Sciences

ISSN: 2455-7749 . Open Access


Importance-based Resilience Assessment and Optimization of Unmanned Ship Swarm System

Importance-based Resilience Assessment and Optimization of Unmanned Ship Swarm System

Hongyan Dui
School of Management, Zhengzhou University, Zhengzhou, 450001, Henan, China.

Kailong Zhang
School of Management, Zhengzhou University, Zhengzhou, 450001, Henan, China.

Wanyun Xia
Zhongyuan Agriculture Insurance Co. Ltd, Zhengzhou, 450000, Henan, China.

DOI https://doi.org/10.33889/IJMEMS.2024.9.3.031

Received on December 24, 2023
  ;
Accepted on April 15, 2024

Abstract

Based on the unmanned ship swarm system, a resilience model for unmanned ship swarms is proposed by comprehensively considering the preventive indicators, robustness indicators, recoverability indicators, and reconfigurability indicators of the swarm system. Firstly, preventive and robust indicators are proposed based on the characteristics of the unmanned ship swarm system, and the improvement of system performance efficiency by redundant unmanned ships is established as a recoverability indicator. Then, reconfigurable indicators are proposed based on importance, and the resilience indicator of the unmanned ship swarm is determined. Finally, a numerical example is used to model and simulate the performance change and capricious process of the unmanned ship swarm. Most of the research on the resilience assessment model of unmanned ship swarms considered too single indicators. The model of the unmanned ship swarm under attack is constructed, and the superiority of the resilience optimization strategy proposed in this paper is verified.

Keywords- Reliability, Resilience, Maintenance, Unmanned ship.

Citation

Dui, H., Zhang, K., & Xia, W. (2024). Importance-based Resilience Assessment and Optimization of Unmanned Ship Swarm System. International Journal of Mathematical, Engineering and Management Sciences, 9(3), 616-631. https://doi.org/10.33889/IJMEMS.2024.9.3.031.