An Operational Chain Optimization Model for Unmanned Swarm Under Dynamic Task Scenarios Driven by Resilience

In persistent battlefield confrontation, combat tasks present dynamic characteristics, i.e., new tasks continuously emerge as old tasks are executed. Therefore, how to enhance the battlefield resilience of unmanned swarm (US) through the rational allocation of limited resources in this scenario has become the focus of research. In response, this paper proposes an operational chain optimization model for the US, which provides modeling and resilience enhancement ideas for the dynamic nature of battlefield tasks. Subsequently, the feasibility and effectiveness of the proposed model is demonstrated under simulation experiments in different attack scenarios.