A Study on the Optimal Deployment of Long-Range Air-Control Radars Using a Benders Decomposition Model

This study proposes an optimal deployment model for long-range air-control radars using a Benders decomposition approach. The model integrates GIS-based terrain visibility analysis and incorporates probabilistic scenarios reflecting radar destruction and failure rates to represent realistic operational uncertainty. A two-stage stochastic optimization framework was developed to minimize total installation cost and detection deficiency across the Korean Air Defense Identification Zone(KADIZ). Computational experiments show that the deterministic model achieved better performance in detection coverage and cost minimization. However, the stochastic model demonstrated its practical value by reflecting uncertainty in radar availability and providing more robust deployment decisions under potential disruption scenarios. The proposed framework contributes to improving the resilience and adaptability of national air surveillance planning under uncertain environments.