Neural Combinatorial Optimization

Neural combinatorial optimization studies how learned models can construct, improve, or guide solutions to problems with many discrete choices and hard constraints, including vehicle routing, scheduling, packing, and graph optimization. Common approaches include graph neural networks and Transformers trained with supervised examples from solvers, reinforcement learning, self-supervised objectives, and newer diffusion or preference-based methods. Many systems combine learned policies with classical search, local improvement, or exact optimization to improve feasibility and solution quality. A central challenge is whether they generalize to larger or unfamiliar instances and offer a practical advantage over strong classical heuristics when training and inference costs are counted.