Collaborative Plan Generation for Intelligent Command and Control Based on the PREA-OODA Dual-Loop Mechanism

This study addresses collaborative plan generation in complex battlefield environments, where task sequencing, heterogeneous resource allocation, and event-driven response are tightly coupled. We develop an intelligent command and control framework that integrates macro-level PREA planning with real-time OODA response. The initial collaborative plan is formulated over discrete time slots with task time windows, precedence relations, resource reliability, and reusable or consumable resources. Its objective jointly considers expected operational effectiveness, resource cost, and priority-weighted completion time. During execution, dynamic decisions are represented by event situations, response actions, route safety, and resource-adjustment cost. A time-indexed 0–1 mixed-integer linear programming (MILP) model and a single-objective genetic algorithm (GA) generate comparable initial plans. Mamdani fuzzy inference, a finite-state Markov decision process (MDP), risk-aware A*, and particle swarm optimization (PSO) then perform situation assessment, action selection, route replanning, and resource adjustment, respectively. Fifty paired replications of an amphibious landing simulation show that the dynamic closed loop increases the task completion rate by 6.33 percentage points and resource utilization by 1.63 percentage points relative to both static methods, while reducing mean response time by 53.70% and the standard deviation of communication delay by 34.91%. The PREA-OODA dual-loop mechanism therefore improves the dynamic adaptability and execution stability of collaborative plans with manageable computational overhead.