Abstract:

To address the challenge of“ plans collapsing with the first shot fired” in large-scale air operations， a collaborative mission schedule management approach is proposed. Initially， the Work Breakdown Structure （WBS） from management science is introduced to decompose the overall combat mission into formation behaviors. Subsequently，focusing on plan formulation， a timeline-based activity on arrow group network representation model for schedule planning is introduced， along with machine-solving algorithms for plans and conflict detection and resolution algorithms for manually crafted plans. Building on this foundation， a multi-objective optimization model targeting mission outcomes and execution processes is established， and the NSGA-Ⅱ algorithm is used to obtain Pareto-optimal plans. Further more， a real-time plan control system is constructed based on closed-loop feedback principles， utilizing a constrained adaptive differential evolution algorithm to derive control strategies. Finally， experimental validation is conducted using publicly available combat scenarios in the“ Mozi” simulation system， with four scenarios set up： no disturbance， disturbance without control， disturbance with control， and disturbance exceeding maximum control range. The experimental results demonstrate that the proposed mission progress management method can generate conflict-free，constraint-satisfying mission schedule plans and enable precise control of mission schedule within the maximum disturbance resistance range， ensuring the successful completion of missions.

Key words: air battle management, collaborative mission schedule management, group network of activity on arrow, multi-objective optimization, adaptive differential evolution algorithm, disturbance resistance control