Imagen de Portada

Policy-Based Reinforcement Learning with Action Masking for Dynamic Job Shop Scheduling under Uncertainty: Handling Random Arrivals and Machine Failures

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Lassoued, Sofiene, Lier, Stefan, Schwung, Andreas
Formato:	Preprint
Publicado:	2026
Materias:	Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Policy-Based Deep Reinforcement Learning Hyperheuristics for Job-Shop Scheduling Problems
por: Lassoued, Sofiene, et al.
Publicado: (2026)

Introducing PetriRL: An Innovative Framework for JSSP Resolution Integrating Petri nets and Event-based Reinforcement Learning
por: Lassoued, Sofiene, et al.
Publicado: (2024)

Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals
por: Tang, Yu, et al.
Publicado: (2026)

Flexible Manufacturing Systems Intralogistics: Dynamic Optimization of AGVs and Tool Sharing Using Coloured-Timed Petri Nets and Actor-Critic RL with Actions Masking
por: Lassoued, Sofiene, et al.
Publicado: (2026)

Learning to Solve Job Shop Scheduling under Uncertainty
por: Infantes, Guillaume, et al.
Publicado: (2024)

Beyond Training: Optimizing Reinforcement Learning Based Job Shop Scheduling Through Adaptive Action Sampling
por: de Puiseau, Constantin Waubert, et al.
Publicado: (2024)

Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling
por: van Remmerden, Jesse, et al.
Publicado: (2024)

Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
por: Zhang, Cong, et al.
Publicado: (2022)

Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem
por: Lee, Jaejin, et al.
Publicado: (2024)

Learning Flexible Job Shop Scheduling under Limited Buffers and Material Kitting Constraints
por: Zhang, Shishun, et al.
Publicado: (2026)

Self-Evaluation for Job-Shop Scheduling
por: Echeverria, Imanol, et al.
Publicado: (2025)

Variational Approach for Job Shop Scheduling
por: Oh, Seung Heon, et al.
Publicado: (2026)

Adaptive Bias Generalized Rollout Policy Adaptation on the Flexible Job-Shop Scheduling Problem
por: Kobrosly, Lotfi, et al.
Publicado: (2025)

HGT-Scheduler: Deep Reinforcement Learning for the Job Shop Scheduling Problem via Heterogeneous Graph Transformers
por: Soykan, Bulent
Publicado: (2026)

Investigation of the Generalisation Ability of Genetic Programming-evolved Scheduling Rules in Dynamic Flexible Job Shop Scheduling
por: Zhu, Luyao, et al.
Publicado: (2026)

Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling
por: Wang, Jiaqi, et al.
Publicado: (2026)

Starjob: Dataset for LLM-Driven Job Shop Scheduling
por: Abgaryan, Henrik, et al.
Publicado: (2025)

Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem
por: Echeverria, Imanol, et al.
Publicado: (2024)

Optimizing Job Shop Scheduling in the Furniture Industry: A Reinforcement Learning Approach Considering Machine Setup, Batch Variability, and Intralogistics
por: Schneevogt, Malte, et al.
Publicado: (2024)

Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods
por: Reijnen, Robbert, et al.
Publicado: (2023)

Layer-Specific Lipschitz Modulation for Fault-Tolerant Multimodal Representation Learning
por: Altinses, Diyar, et al.
Publicado: (2026)

ReflecSched: Solving Dynamic Flexible Job-Shop Scheduling via LLM-Powered Hierarchical Reflection
por: Cao, Shijie, et al.
Publicado: (2025)

An Analysis of the Coordination Gap between Joint and Modular Learning for Job Shop Scheduling with Transportation Resources
por: Link, Moritz, et al.
Publicado: (2026)

Distributed Stackelberg Strategies in State-based Potential Games for Autonomous Decentralized Learning Manufacturing Systems
por: Yuwono, Steve, et al.
Publicado: (2024)

Self-Labeling the Job Shop Scheduling Problem
por: Corsini, Andrea, et al.
Publicado: (2024)

Gradient-based Learning in State-based Potential Games for Self-Learning Production Systems
por: Yuwono, Steve, et al.
Publicado: (2024)

Graph Neural Networks for Job Shop Scheduling Problems: A Survey
por: Smit, Igor G., et al.
Publicado: (2024)

DyRo-MCTS: A Robust Monte Carlo Tree Search Approach to Dynamic Job Shop Scheduling
por: Chen, Ruiqi, et al.
Publicado: (2025)

An Expectation-Maximization Algorithm-based Autoregressive Model for the Fuzzy Job Shop Scheduling Problem
por: Wang, Yijian, et al.
Publicado: (2025)

Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems
por: Yuwono, Steve, et al.
Publicado: (2024)

Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem
por: Zhang, Cong, et al.
Publicado: (2024)

Decision Transformer for Enhancing Neural Local Search on the Job Shop Scheduling Problem
por: de Puiseau, Constantin Waubert, et al.
Publicado: (2024)

Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop Scheduling
por: Li, Sirui, et al.
Publicado: (2025)

Stabilizing Multimodal Autoencoders: A Theoretical and Empirical Analysis of Fusion Strategies
por: Altinses, Diyar, et al.
Publicado: (2025)

Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems
por: Smit, Igor G., et al.
Publicado: (2024)

Real Time Self-Tuning Adaptive Controllers on Temperature Control Loops using Event-based Game Theory
por: Yuwono, Steve, et al.
Publicado: (2025)

Low-Cost Labels, Reliable Choices: Rollout-Calibrated Hyper-Heuristics for Job Shop Scheduling
por: Wei, Junhao, et al.
Publicado: (2026)

Investigating the Monte-Carlo Tree Search Approach for the Job Shop Scheduling Problem
por: Boveroux, Laurie, et al.
Publicado: (2025)

Instance Configuration for Sustainable Job Shop Scheduling
por: Perez, Christian, et al.
Publicado: (2024)

ReLA: Representation Learning and Aggregation for Job Scheduling with Reinforcement Learning
por: Kwan, Zhengyi, et al.
Publicado: (2026)