label-cost

This paper proposes a gated hyper-heuristic for job shop scheduling that uses regret-normalized rollout labels and contextual KNN uncertainty estimates to reduce label generation costs and avoid switching away from strong default rules unless the predicted improvement is credible. Experiments show the gated selector achieves low mean relative percentage deviation while significantly reducing computational cost.