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Singapore Management University
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By explicitly modeling operation sequences on machines with a novel heterogeneous disjunctive graph, MIStar significantly boosts the performance of improvement-based methods for flexible job-shop scheduling.
Achieve state-of-the-art results on multi-task vehicle routing problems by dynamically adapting to evolving constraints, even when those constraints are unseen during training.
Neural routing solvers can now efficiently tackle hard constraints thanks to Construct-and-Refine (CaR), which slashes the refinement steps needed by 500x while boosting solution quality.
