Scheduling with Heterogeneous Relations: Reinforcement Learning for Complex Hierarchical Manufacturing Systems

Hierarchical manufacturing scheduling is particularly challenging in practice, as complex production structures and heterogeneous operational constraints give rise to tightly coupled decisions that extend substantially beyond the standard flexible job shop scheduling problem.To address this challenge, we propose CRST, a neural architecture that models scheduling states through multiple complementary relations. CRST performs relation-specific reasoning over workflow dependencies, machine competition, and reusable resource interactions, and then adaptively combines these complementary contexts through Relation Mix to measure the importance of these relations. A pair decoder directly evaluates feasible task machine assignments, while the environment enforces exact feasibility under hard scheduling constraints.Additionally, we introduce a decomposition strategy that substantially reduces memory consumption during large-scale inference while preserving global constraint consistency.Experiments show that CRST achieves solution quality comparable to CP-SAT on standard size instances and becomes more competitive on larger problems, outperforming CP-SAT under a fixed 30-minute search budget by up to 8.48% while requiring only tens of seconds of inference.