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The COMPASS algorithm for OCTSP is introduced, which combines search with learning-accelerated routing by orchestrating parallel sub-solvers and can reach exact solutions in time exponential in cluster size rather than instance size.
This work introduces an optimization framework where a reinforcement learning agent is trained on prior instances and quickly generates initial solutions, which are then further optimized by a genetic algorithm, enabling real-time and interactive routing at scale.
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