Learning with Resource-Capacity Reasoning for Flexible Job-Shop Scheduling

Deep Reinforcement Learning (DRL) has emerged as a promising paradigm for Flexible Job-Shop Scheduling (FJSP). However, existing DRL-based schedulers predominantly rely on learned state representations to evaluate candidate decisions, with limited explicit reasoning about whether an action preserves sufficient resource capacity to avoid future bottlenecks and complete the remaining operations. In this paper, we introduce Resource-Capacity Reasoning (RCR), an encoder-agnostic framework that incorporates analytical capacity information into DRL-based scheduling without modifying the underlying encoder. RCR constructs a Balanced-Capacity Descriptor (BCD) that characterizes how each candidate assignment changes residual capacity pressure and redistributes bottlenecks, augmenting the corresponding action representation used by the policy network. Based on the same capacity conditions, RCR further introduces a Completion-Loss Certificate (CLC) and incorporates it as an auxiliary penalty while preserving the original makespan-oriented reward. Extensive experiments across representative DRL-based scheduling methods demonstrate consistent improvements in solution quality. Moreover, RCR naturally extends to the Job-Shop Scheduling Problem (JSSP), confirming its applicability for different scheduling variants.