CReSCENT for Long-Term Value-Driven Scheduling in Multi-Layer Industrial Networks with Milestone-Triggered Rewards

This paper studies long-term value-driven scheduling in multi-layer industrial networks where useful external reward is released mainly at milestone transitions. The delayed feedback causes supervision degeneracy before milestones, because many trajectory prefixes receive the same external return even when their downstream potential is different. We formulate the setting as a time-delayed multi-industrial-chain Markov decision process and present CReSCENT as a milestone-aware structural exploration framework rather than a new reinforcement learning principle. CReSCENT combines macro structural feature construction, contrastive representation learning, milestone-weighted online clustering, and cross-layer credit allocation. It moves intrinsic learning from raw observations to milestone-relevant structural states, then distributes the resulting signal to layers according to their contribution to cross-layer progress. The revised evaluation gives the simulator transition rules, a formal utility metric, an external-reward-only baseline, confidence intervals, and statistical tests. Experiments show that CReSCENT improves utility across layer, task-load, worker-count, and episode-length perturbations against ETA-PSI, EMU, MIMEx, and the external-only baseline. Sensitivity studies further show that the clustering radius and credit-allocation weights have stable operating ranges.