Optimization strategy for batch launch deployment of large-scale low earth orbit constellations based on multimodal transportation network model

Large-scale Low Earth Orbit (LEO) constellations have become a focal point due to their capability to provide round-the-clock high-fidelity information services. However, their efficient and economical batch deployment faces severe challenges stemming from growing demands and multiple constraints, with existing methods struggling to effectively address the computational complexity in large-scale scenarios. Addressing this pressing need, this study proposes an innovative deployment optimization framework. Its core lies in constructing a novel partial time-expanded network that significantly reduces (over 90%) redundant links through feasibility pruning and hierarchical aggregation strategies, effectively tackling the exponential growth of constraints inherent in traditional models, and proposing an efficient hybrid algorithm integrating column generation and A* search, which, combined with a subproblem filter, significantly enhances the solution efficiency and scalability for large-scale problems. The framework supports dual-channel, multi-configuration rocket strategies and achieves flexible deployment under multiple mission triggers through weighted optimization. The research demonstrates that the proposed method can effectively reduce deployment costs, improve optimization efficiency, and provide reliable decision support for large-scale constellation deployment.