Genetic Programming with Reinforcement Learning Trained Transformer for Real-World Dynamic Scheduling Problems
Fuente:
arXiv
Guardado en:
| Autores principales: | Chen, Xinan, Qu, Rong, Dong, Jing, Bai, Ruibin, Jin, Yaochu |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Preference-Agile Multi-Objective Optimization for Real-time Vehicle Dispatching
por: Jin, Jiahuan, et al.
Publicado: (2026)
por: Jin, Jiahuan, et al.
Publicado: (2026)
PGU-SGP: A Pheno-Geno Unified Surrogate Genetic Programming For Real-life Container Terminal Truck Scheduling
por: Tan, Leshan, et al.
Publicado: (2025)
por: Tan, Leshan, et al.
Publicado: (2025)
MeLA: A Metacognitive LLM-Driven Architecture for Automatic Heuristic Design
por: Qiu, Zishang, et al.
Publicado: (2025)
por: Qiu, Zishang, et al.
Publicado: (2025)
The Pump Scheduling Problem: A Real-World Scenario for Reinforcement Learning
por: Donâncio, Henrique, et al.
Publicado: (2022)
por: Donâncio, Henrique, et al.
Publicado: (2022)
Reinforcement Learning for Scalable Train Timetable Rescheduling with Graph Representation
por: Yue, Peng, et al.
Publicado: (2024)
por: Yue, Peng, et al.
Publicado: (2024)
LacaDM: A Latent Causal Diffusion Model for Multiobjective Reinforcement Learning
por: Yan, Xueming, et al.
Publicado: (2025)
por: Yan, Xueming, et al.
Publicado: (2025)
FedSlate:A Federated Deep Reinforcement Learning Recommender System
por: Deng, Yongxin, et al.
Publicado: (2024)
por: Deng, Yongxin, et al.
Publicado: (2024)
A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints
por: Hoss, Jonathan, et al.
Publicado: (2025)
por: Hoss, Jonathan, et al.
Publicado: (2025)
Investigation of the Generalisation Ability of Genetic Programming-evolved Scheduling Rules in Dynamic Flexible Job Shop Scheduling
por: Zhu, Luyao, et al.
Publicado: (2026)
por: Zhu, Luyao, et al.
Publicado: (2026)
Constrained Multi-objective Optimization with Deep Reinforcement Learning Assisted Operator Selection
por: Ming, Fei, et al.
Publicado: (2024)
por: Ming, Fei, et al.
Publicado: (2024)
Robustness of Probabilistic Models to Low-Quality Data: A Multi-Perspective Analysis
por: Peng, Liu, et al.
Publicado: (2025)
por: Peng, Liu, et al.
Publicado: (2025)
Diffusion Model-Based Multiobjective Optimization for Gasoline Blending Scheduling
por: Fang, Wenxuan, et al.
Publicado: (2024)
por: Fang, Wenxuan, et al.
Publicado: (2024)
Guiding Multi-agent Multi-task Reinforcement Learning by a Hierarchical Framework with Logical Reward Shaping
por: Liu, Chanjuan, et al.
Publicado: (2024)
por: Liu, Chanjuan, et al.
Publicado: (2024)
HGT-Scheduler: Deep Reinforcement Learning for the Job Shop Scheduling Problem via Heterogeneous Graph Transformers
por: Soykan, Bulent
Publicado: (2026)
por: Soykan, Bulent
Publicado: (2026)
Investigating Constraint Programming and Hybrid Methods for Real World Industrial Test Laboratory Scheduling
por: Geibinger, Tobias, et al.
Publicado: (2019)
por: Geibinger, Tobias, et al.
Publicado: (2019)
Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem
por: Echeverria, Imanol, et al.
Publicado: (2024)
por: Echeverria, Imanol, et al.
Publicado: (2024)
Enhancing Vision-Language Model Training with Reinforcement Learning in Synthetic Worlds for Real-World Success
por: Bredis, George, et al.
Publicado: (2025)
por: Bredis, George, et al.
Publicado: (2025)
Scheduling Your LLM Reinforcement Learning with Reasoning Trees
por: Wang, Hong, et al.
Publicado: (2025)
por: Wang, Hong, et al.
Publicado: (2025)
Policy-Based Deep Reinforcement Learning Hyperheuristics for Job-Shop Scheduling Problems
por: Lassoued, Sofiene, et al.
Publicado: (2026)
por: Lassoued, Sofiene, et al.
Publicado: (2026)
EmoDM: A Diffusion Model for Evolutionary Multi-objective Optimization
por: Yan, Xueming, et al.
Publicado: (2024)
por: Yan, Xueming, et al.
Publicado: (2024)
Biologically Plausible Brain Graph Transformer
por: Peng, Ciyuan, et al.
Publicado: (2025)
por: Peng, Ciyuan, et al.
Publicado: (2025)
Knowledge-Assisted Dual-Stage Evolutionary Optimization of Large-Scale Crude Oil Scheduling
por: Zhang, Wanting, et al.
Publicado: (2024)
por: Zhang, Wanting, et al.
Publicado: (2024)
RLVR-World: Training World Models with Reinforcement Learning
por: Wu, Jialong, et al.
Publicado: (2025)
por: Wu, Jialong, et al.
Publicado: (2025)
Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem
por: Lee, Jaejin, et al.
Publicado: (2024)
por: Lee, Jaejin, et al.
Publicado: (2024)
Harmonizing Real-Time Constraints and Long-Horizon Reasoning: An Asynchronous Agentic Framework for Dynamic Scheduling
por: Cao, Shijie, et al.
Publicado: (2026)
por: Cao, Shijie, et al.
Publicado: (2026)
NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning with Extended Realistic Scenarios
por: Gao, Songyi, et al.
Publicado: (2025)
por: Gao, Songyi, et al.
Publicado: (2025)
Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learning
por: Shen, Ya, et al.
Publicado: (2024)
por: Shen, Ya, et al.
Publicado: (2024)
Reinforced Linear Genetic Programming
por: Mukhammadnaim, Urmzd
Publicado: (2026)
por: Mukhammadnaim, Urmzd
Publicado: (2026)
World-Gymnast: Training Robots with Reinforcement Learning in a World Model
por: Sharma, Ansh Kumar, et al.
Publicado: (2026)
por: Sharma, Ansh Kumar, et al.
Publicado: (2026)
GASE: Graph Attention Sampling with Edges Fusion for Solving Vehicle Routing Problems
por: Wang, Zhenwei, et al.
Publicado: (2024)
por: Wang, Zhenwei, et al.
Publicado: (2024)
Reinforcing the World's Edge: A Continual Learning Problem in the Multi-Agent-World Boundary
por: Malenfant, Dane
Publicado: (2026)
por: Malenfant, Dane
Publicado: (2026)
Scalable Knee-Point Guided Activity Group Selection in Multi-Tree Genetic Programming for Dynamic Multi-Mode Project Scheduling
por: Tian, Yuan, et al.
Publicado: (2026)
por: Tian, Yuan, et al.
Publicado: (2026)
A Structure-Aware Lane Graph Transformer Model for Vehicle Trajectory Prediction
por: Zhanbo, Sun, et al.
Publicado: (2024)
por: Zhanbo, Sun, et al.
Publicado: (2024)
Genetic-based Constraint Programming for Resource Constrained Job Scheduling
por: Nguyen, Su, et al.
Publicado: (2024)
por: Nguyen, Su, et al.
Publicado: (2024)
Training High-Level Schedulers with Execution-Feedback Reinforcement Learning for Long-Horizon GUI Automation
por: Deng, Zehao, et al.
Publicado: (2025)
por: Deng, Zehao, et al.
Publicado: (2025)
From Programs to Poses: Factored Real-World Scene Generation via Learned Program Libraries
por: Hsu, Joy, et al.
Publicado: (2025)
por: Hsu, Joy, et al.
Publicado: (2025)
Surrogate-Assisted Genetic Programming with Rank-Based Phenotypic Characterisation for Dynamic Multi-Mode Project Scheduling
por: Tian, Yuan, et al.
Publicado: (2026)
por: Tian, Yuan, et al.
Publicado: (2026)
TriCon-SF: A Triple-Shuffle and Contribution-Aware Serial Federated Learning Framework for Heterogeneous Healthcare Data
por: Yan, Yuping, et al.
Publicado: (2025)
por: Yan, Yuping, et al.
Publicado: (2025)
RLNVR: Reinforcement Learning from Non-Verified Real-World Rewards
por: Krishnan, Rohit, et al.
Publicado: (2025)
por: Krishnan, Rohit, et al.
Publicado: (2025)
Safe Reinforcement Learning for Real-World Engine Control
por: Bedei, Julian, et al.
Publicado: (2025)
por: Bedei, Julian, et al.
Publicado: (2025)
Ejemplares similares
-
Preference-Agile Multi-Objective Optimization for Real-time Vehicle Dispatching
por: Jin, Jiahuan, et al.
Publicado: (2026) -
PGU-SGP: A Pheno-Geno Unified Surrogate Genetic Programming For Real-life Container Terminal Truck Scheduling
por: Tan, Leshan, et al.
Publicado: (2025) -
MeLA: A Metacognitive LLM-Driven Architecture for Automatic Heuristic Design
por: Qiu, Zishang, et al.
Publicado: (2025) -
The Pump Scheduling Problem: A Real-World Scenario for Reinforcement Learning
por: Donâncio, Henrique, et al.
Publicado: (2022) -
Reinforcement Learning for Scalable Train Timetable Rescheduling with Graph Representation
por: Yue, Peng, et al.
Publicado: (2024)