1 citations · 1 across the 6 of their papers we have counts for
4 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…
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…