RESCHED: Rethinking Flexible Job Shop Scheduling from a Transformer-based Architecture with Simplified States

Neural approaches to the Flexible Job Shop Scheduling Problem (FJSP), particularly those based on deep reinforcement learning (DRL), have gained growing attention in recent years. Yet existing methods often rely on cumbersome state representations (i.e. sometimes requiring more than 20 handcrafted features) and suboptimal neural architectures. We introduce \textsc{ReSched}, a minimalist DRL framework that rethinks both the scheduling formulation and model design. First, we revisit the Markov Decision Process (MDP) formulation of FJSP, reducing the state to just four essential features and replacing historical dependencies with a graph structure that directly encodes intra-job operation relationships. Second, we employ Transformer blocks with dot-product attention, augmented by three lightweight but effective architectural modifications tailored to scheduling. Extensive experiments show that \textsc{ReSched} outperforms classical dispatching rules and state-of-the-art DRL methods on FJSP. Moreover, \textsc{ReSched} generalizes well to the Job Shop Scheduling Problem (JSSP) and the Flexible Flow Shop Scheduling Problem (FFSP), achieving competitive performance against neural baselines specifically designed for these variants.