AI that generates feasible plans for delivery, production, and workforce scheduling without external solvers

From parcel delivery routes and factory production schedules to hospital duty rosters, many real-world planning tasks require solutions that satisfy numerous operational constraints. KAIST researchers led by Professor Min-Soo Kim from the School of Computing have developed RL-SPH (Reinforcement Learning-based Start Primal Heuristic), a reinforcement learning technique that trains AI to independently produce feasible plans without relying on an external solver.