7 papers
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…
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…
DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control
Mohammadhossein Malmir, Josip Josifovski, Noah Klarmann +1
Delayed Markov decision processes (DMDPs) fulfill the Markov property by augmenting the state space of agents with a finite time window of recently committed actions. In reliance o…
A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints
Jonathan Hoss, Felix Schelling, Noah Klarmann
The classical Job Shop Scheduling Problem (JSSP) focuses on optimizing makespan under deterministic constraints. Real-world production environments introduce additional complexitie…