A Heterogeneous Graph Reinforcement Learning Framework With Question-Aware Neighborhood Aggregation and Interoption Prompt Attention for Dynamic Flexible Job Shop Scheduling Problem

Dynamic flexible job shop scheduling (DFJSS) problem is an important scenario in intelligent manufacturing domain with the requirement of real-time decision-making under complex constraints. Existing approaches struggle to handle dynamic environments and heterogeneous job-resource relationships effectively while keeping optimum scheduling performance simultaneously. To address this challenge, a heterogeneous graph reinforcement learning framework combining question-aware neighborhood aggregation and interoption prompt attention (QIHGRL) is presented to address the DFJSS problem with new job insertions and variable processing times to minimize total tardiness. The optimization objectives are transformed into attention-guided signals by the question-aware neighborhood aggregation module to improve feature representation in the heterogeneous graph encoding stage of the QIHGRL. The competition and collaboration among scheduling actions are explicitly modeled by the interoption prompt attention layer in the policy optimization phase of the QIHGRL via adjusting action selection weights through a multihead attention mechanism to balance exploration and exploitation of the candidates in the population of the QIHGRL. The experimental results testified that the performance and efficiency of the QIHGRL outperforms that of the state of the arts algorithms.