14 citations · 17 across the 12 of their papers we have counts for
6 papers · 1 filter
PORL: Pretrained Offline Reinforcement Learning for the Job Shop Scheduling Problem
Mateo Toro Diz, Jonathan Hoss, Noah Klarmann
The Job Shop Scheduling Problem (JSSP) is a fundamental combinatorial optimization problem in industrial optimization. This work introduces Pretrained Offline Reinforcement Learnin…
Curriculum Learning with GNN-based Reinforcement Learning for Job Shop Scheduling
Jayakrishnan K. Vasudevan, Jonathan Hoss, Noah Klarmann
The job shop scheduling problem is a challenging combinatorial optimization problem, and recent reinforcement learning approaches using graph neural networks have shown promise for…
Measurement-Calibrated Multi-Camera Fusion for Vision-Based Indoor Localization
Mateo Toro Diz, Jonathan Hoss, Noah Klarmann
Indoor vision-based localization systems are affected by detection noise, occlusions, and limited camera coverage, leading to uncertainty at multiple stages of the pipeline. While…
Bridging the Sim-to-Real Gap in Reinforcement Learning-Based Industrial Dispatching through Execution Semantics
Jonathan Hoss, Noah Klarmann
Event-driven scheduling policies are increasingly deployed in industrial environments, where decisions are made under asynchronous and partially observed system states. As a result…
An Analysis of the Coordination Gap between Joint and Modular Learning for Job Shop Scheduling with Transportation Resources
Moritz Link, Jonathan Hoss, Noah Klarmann
Efficient job-shop scheduling with transportation resources is critical for high-performance manufacturing. With the rise of "decentralized factories", multi-agent reinforcement le…
Scalable Production Scheduling: Linear Complexity via Unified Homogeneous Graphs
Jonathan Hoss, Moritz Link, Noah Klarmann
Efficiently solving the Job Shop Scheduling Problem in real-world industrial applications requires policies that are both computationally lean and topologically robust. While Reinf…