Cover Image

Learning to Optimize Job Shop Scheduling Under Structural Uncertainty

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Rui, Niu, Jianwei, Liu, Xuefeng, Tang, Shaojie, Yuan, Jing
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Why Go Full? Elevating Federated Learning Through Partial Network Updates
by: Wang, Haolin, et al.
Published: (2024)

Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation
by: Guo, Wenkai, et al.
Published: (2025)

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

The Other Side of the Coin: Unveiling the Downsides of Model Aggregation in Federated Learning from a Layer-peeled Perspective
by: Zhu, Guogang, et al.
Published: (2025)

From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning
by: Wu, Xinghao, et al.
Published: (2026)

Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
by: Wu, Xinghao, et al.
Published: (2024)

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

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

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

DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations
by: Zhu, Guogang, et al.
Published: (2024)

Mamba Meets Scheduling: Learning to Solve Flexible Job Shop Scheduling with Efficient Sequence Modeling
by: Cao, Zhi, et al.
Published: (2026)

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

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

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

Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning
by: Wu, Xinghao, et al.
Published: (2025)

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

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

Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition
by: Wu, Xinghao, et al.
Published: (2024)

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

Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning
by: Zhu, Guogang, et al.
Published: (2024)

The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning
by: Wu, Xinghao, et al.
Published: (2024)

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

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

Learning Submodular Sequencing from Samples
by: Yuan, Jing, et al.
Published: (2024)

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

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

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

RESCHED: Rethinking Flexible Job Shop Scheduling from a Transformer-based Architecture with Simplified States
by: Xiao, Xiangjie, et al.
Published: (2026)

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

The Power of Second Chance: Personalized Submodular Maximization with Two Candidates
by: Yuan, Jing, et al.
Published: (2024)

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

Improved Learning Rates for Stochastic Optimization
by: Li, Shaojie, et al.
Published: (2021)

Graph-RHO: Critical-path-aware Heterogeneous Graph Network for Long-Horizon Flexible Job-Shop Scheduling
by: Li, Yujie, et al.
Published: (2026)

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

Optimizing Electric Bus Charging Scheduling with Uncertainties Using Hierarchical Deep Reinforcement Learning
by: Qi, Jiaju, et al.
Published: (2025)

Fairness-Aware Job Scheduling for Multi-Job Federated Learning
by: Shi, Yuxin, et al.
Published: (2024)

LLM-Upgraded Graph Reinforcement Learning for Carbon-Aware Job Scheduling in Smart Manufacturing
by: Yang, Zhiying, et al.
Published: (2025)

Multi-Agent Reinforcement Learning for Intraday Operating Rooms Scheduling under Uncertainty
by: Liu, Kailiang, et al.
Published: (2025)

Robust Adversarial Policy Optimization Under Dynamics Uncertainty
by: Kim, Mintae, et al.
Published: (2026)