Efficient job-shop scheduling with transportation resources is critical for high-performance manufacturing. With the rise of "decentralized factories", multi-agent reinforcement learning has emerged as a promising approach for the combined scheduling of production and transportation tasks. Prior work has largely focused on developing novel cooperative architectures while overlooking the question of when joint training is necessary. Joint training denotes the simultaneous training of job and automatic guided vehicle scheduling agents, whereas modular training involves independently training each agent followed by post-hoc integration. In this study, we systematically investigate the conditions under which joint training is essential for optimal performance in the job-shop scheduling problem with transportation resources. Through a rigorous sensitivity analysis of resource scarcity and temporal dominance, we quantify the coordination gap -- the performance difference between these two training modalities. In our evaluation, joint training outperforms the majority of dispatching rule combinations and modular training approaches. However, the coordination gap advantage diminishes in bottleneck environments, particularly under severe transport and processing constraints. These findings indicate that modular training represents a viable alternative in environments where a single scheduling task dominates. Overall, our work provides practical guidance for selecting between training modalities based on environmental conditions, enabling decision-makers to optimize reinforcement learning-based scheduling performance.

翻译：含运输资源的高效车间作业调度对于高性能制造至关重要。随着“分散化工厂”的兴起，多智能体强化学习已成为联合调度生产与运输任务的一种有前景的方法。以往研究主要聚焦于开发新型协作架构，却忽视了联合训练何时为必要的这一问题。联合训练指同时训练作业调度智能体与自动导引车调度智能体，而模块化训练则指独立训练各智能体后再进行事后整合。在本研究中，我们系统探究了在含运输资源的车间作业调度问题中，联合训练对于实现最优性能的必要条件。通过对资源稀缺性和时间主导性的严格敏感性分析，我们量化了协调差距——即上述两种训练模式之间的性能差异。在评估中，联合训练优于大多数调度规则组合与模块化训练方法。然而，在瓶颈环境下，尤其在运输与加工约束严苛时，协调差距优势会减弱。这些发现表明，在单一调度任务占主导的环境下，模块化训练是一种可行的替代方案。总体而言，本研究为根据环境条件选择训练模式提供了实用指导，使决策者能够优化基于强化学习的调度性能。