14 citations · 17 across the 12 of their papers we have counts for
14 papers
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…
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…
AI-Driven Multi-Stage Computer Vision System for Defect Detection in Laser-Engraved Industrial Nameplates
Adhish Anitha Vilasan, Stephan Jäger, Noah Klarmann
Automated defect detection in industrial manufacturing is essential for maintaining product quality and minimizing production errors. In air disc brake manufacturing, ensuring the…
Optimizing Job Shop Scheduling in the Furniture Industry: A Reinforcement Learning Approach Considering Machine Setup, Batch Variability, and Intralogistics
Malte Schneevogt, Karsten Binninger, Noah Klarmann
This paper explores the potential application of Deep Reinforcement Learning in the furniture industry. To offer a broad product portfolio, most furniture manufacturers are organiz…
Autoencoder-Based Visual Anomaly Localization for Manufacturing Quality Control
Devang Mehta, Noah Klarmann
Manufacturing industries require efficient and voluminous production of high-quality finished goods. In the context of Industry 4.0, visual anomaly detection poses an optimistic so…
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 Reinforcement Learning Approach for Scheduling Problems With Improved Generalization Through Order Swapping
Deepak Vivekanandan, Samuel Wirth, Patrick Karlbauer +1
The scheduling of production resources (such as associating jobs to machines) plays a vital role for the manufacturing industry not only for saving energy but also for increasing t…
Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks
Josip Josifovski, Mohammadhossein Malmir, Noah Klarmann +3
Randomization is currently a widely used approach in Sim2Real transfer for data-driven learning algorithms in robotics. Still, most Sim2Real studies report results for a specific r…
Artificial Intelligence Narratives: An Objective Perspective on Current Developments
Noah Klarmann
This work provides a starting point for researchers interested in gaining a deeper understanding of the big picture of artificial intelligence (AI). To this end, a narrative is con…