An Analysis of the Coordination Gap between Joint and Modular Learning for Job Shop Scheduling with Transportation Resources
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, the joint training can produce superior performance compared to the best-performing combinations of dispatching rules and modular training. 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.
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Computer Science > Artificial Intelligence arXiv:2604.24117 (cs) [Submitted on 27 Apr 2026] Title:An Analysis of the Coordination Gap between Joint and Modular Learning for Job Shop Scheduling with Transportation Resources Authors:Moritz Link, Jonathan Hoss, Noah Klarmann View a PDF of the paper titled An Analysis of the Coordination Gap between Joint and Modular Learning for Job Shop Scheduling with Transportation Resources, by Moritz Link and 2 other authors View PDF Abstract: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, the joint training can produce superior performance compared to the best-performing combinations of dispatching rules and modular training. 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. Comments: Supported by the Chips Joint Undertaking and its members, including top-up funding by National Authorities, within the Cynergy4MIE project (Grant Agreement No. 101140226). This work has been submitted to the IEEE for possible publication Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.24117 [cs.AI] (or arXiv:2604.24117v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2604.24117 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Moritz Link [view email] [v1] Mon, 27 Apr 2026 07:12:34 UTC (77 KB) Full-text links: Access Paper: View a PDF of the paper titled An Analysis of the Coordination Gap between Joint and Modular Learning for Job Shop Scheduling with Transportation Resources, by Moritz Link and 2 other authorsView PDFTeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai…
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