The Optimal Decision for Course of Action under Dynamic Warfare Environment

In dynamic environment of modern warfare, commanders require real-time adaptive decision-making capabilities to maintain combat effectiveness amidst evolving situational factors. This study proposes a real-time recursive optimization framework for course of action (COA) that integrates Bayesian network modeling with a hierarchical analysis-based evaluation system. The methodology addresses two critical operational challenges: continuous battlefield state transitions and commanders' dynamic intent adjustments during mission execution. Through feedback-driven optimization mechanisms, the proposed approach enhances COA adaptability while preserving strategic coherence. Simulation results demonstrate a 22.6% measurable improvement in scheme evaluation metrics compared to static planning methods. The recursive optimization process effectively mitigates environmental uncertainty impacts, maintaining solution optimality throughout combat operations despite emergent threats and command priority shifts.