Graph neural architecture for dynamic hybrid flowshop: Addressing stochastic events and uncertain processing sequences

Dynamic hybrid flow shop scheduling (DHFSP) presents substantial difficulties in attaining optimal production efficiency. These challenges arise particularly when addressing indeterminate job sequences and stochastic events. Current dynamic scheduling methodologies employing deep reinforcement learning (DRL) predominantly target static-scale problems while exhibiting limited scalability to evolving complexities in industrial production systems. To enable responsive shop-floor decisions, this work introduces a hierarchical interaction attention-driven multi-graph network (HIAMG) framework engineered for DHFSP with uncertain job sequences and stochastic events. The proposed architecture implements graph-structured scenario encoding to address neural network dimension constraints. It is coupled with a novel graph embedding paradigm that hierarchically models job-machine interdependencies. Through explicit incorporation of stochastic events in scheduling simulations, our approach achieves enhanced operational fidelity reflecting real manufacturing settings. Integrating attention-based mechanisms further empowers the model to dynamically prioritize critical scheduling parameters and self-tune to environmental fluctuations. Comprehensive simulations across diverse operational regimes demonstrate that HIAMG surpasses conventional scheduling benchmarks in both performance metrics and configuration adaptability.