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A resilience-driven two-stage operational chain optimization model for unmanned weapon system-of-systems under limited resource environments
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School of Systems Science and Engineering, Sun Yat-sen University, China
 
 
Submission date: 2024-01-11
 
 
Final revision date: 2024-03-22
 
 
Acceptance date: 2024-05-01
 
 
Online publication date: 2024-05-11
 
 
Publication date: 2024-05-11
 
 
Corresponding author
Xuebin Zhuang   

School of Systems Science and Engineering, Sun Yat-sen University, China
 
 
Eksploatacja i Niezawodność – Maintenance and Reliability 2024;26(3):188198
 
HIGHLIGHTS
  • A task-oriented resilience metric is proposed to characterize the impact of operational chain changes on the resilience of weapon systems.
  • A two-stage operational chain optimization model is constructed, considering the behaviours of edge nodes and command nodes in different resilience phases.
  • The impact of operational chain optimization on resilience is analyzed in terms of different attack time, intensity, scenarios, task numbers and structures.
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ABSTRACT
Enhancing the battlefield resilience of unmanned weapon system-of-systems (UWSoS) through structural reconstruction requires scheduling additional physical resources. However, they are scarce in limited resource environments. To address the challenge of resource constraints, this paper focuses on improving the resilience of UWSoS by optimizing the operational chain of tasks after a disruption. First, a task-oriented resilience metric is proposed to characterize the impact of operational chain variations on UWSoS resilience. Based on this, a two-stage operational chain optimization model for UWSoS under limited resource environments is established, which considers the optimization actions of the edge node and rear command node in different resilience phases after the interruption for resilience enhancement. Finally, extensive simulation experiments validate the effectiveness and superiority of the proposed model. This work can support decision-makers in developing new task plans in disruption scenarios and serve as a transition approach to enhance UWSoS resilience.
ACKNOWLEDGEMENTS
This research was supported by the Science and Technology on Information System Engineering Laboratory (No.05202007).
 
CITATIONS (4):
1.
An Operational Chain Optimization Model for Unmanned Swarm Under Dynamic Task Scenarios Driven by Resilience
Yuanfu Zhong, Yuepeng Cai, Xuebin Zhuang
2024 IEEE International Conference on Unmanned Systems (ICUS)
 
2.
Optimizing energy consumption in heating systems through advanced machine learning and hybrid optimization for precision heating load prediction
Chengcheng Cai, Na Feng, Qianqian Liu
Proceedings of the Indian National Science Academy
 
3.
RSR-SESoS:A robust space resilience enhancement framework in spatial equipment system-of-systems
Guoyu Ning, Renjie Xu, Chengyun Xiong, Minghao Li, Jichao Li, Kewei Yang
Expert Systems with Applications
 
4.
A Multi-Phase Resilience Evaluation Method for Cross-Domain Unmanned Swarms
Heyuan Li, Hao Li, Mingxin Hou, Xiaohui Yang, Guanghan Bai
2025 11th International Symposium on System Security, Safety, and Reliability (ISSSR)
 
eISSN:2956-3860
ISSN:1507-2711
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