Job Shop Scheduling

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Job shop scheduling studies how to order operations across machines while respecting job precedence and resource constraints; flexible variants also choose among eligible machines. Methods include dispatching rules, constraint programming and search heuristics, as well as deep reinforcement learning with graph neural networks or Transformers. Recent work also addresses dynamic arrivals, buffers, transport and time lags, using benchmarks and production data to test performance under realistic conditions. A central challenge is balancing solution quality and real-time efficiency with reliable generalization across instance sizes and changing conditions, while exact solvers remain strong references where their cost is practical.

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23 papers0 papers in the last four weeks, down 100% on the four weeks before. 0.0% of all new papers.

APEX: An Extensible Model for Agent-Assisted Production Scheduling
Sep 27, 2026Felix J. Grumbach, Stefan GörlitzMulti-Objective Evolutionary Optimization Combinatorial Optimization
Qunevo GmbH, Germany
PORL: Pretrained Offline Reinforcement Learning for the Job Shop Scheduling Problem
Sep 25, 2026Mateo Toro Diz, Jonathan Hoss, Noah KlarmannRL for Combinatorial Optimization Reinforcement Learning
Department of Industrial Engineering Rosenheim University of Applied Sciences Rosenheim, Germany
Curriculum Learning with GNN-based Reinforcement Learning for Job Shop Scheduling
Sep 23, 2026Jayakrishnan K. Vasudevan, Jonathan Hoss, Noah KlarmannReinforcement Learning Curriculum RL
Rosenheim University of Applied Sciences
A Shop Floor Production Scheduling Case based on RFID-supported Smart Factory
Aug 17, 2026Zhihui Chen, Yize Sun, Yuhao Dong +2Smart Manufacturing Real-Time Scheduling
Department of Mathematics The University of Hong Kong Hong Kong, China · Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Hong Kong, China
PLAN: Parallel Liquid-Inspired Approximation Network for Efficient Representation Learning in Flexible Job Shop Scheduling
Aug 4, 2026Dhivya Dharshini Kannan, Wei Zhang, Jieyi Bi +5Efficient Neural Network Inference Liquid Neural Networks
Singapore Institute of Technology (SIT) · Nanyang Technological University (NTU) · Shanghai Jiao Tong University +1
Time-Lag-Aware Deep Reinforcement Learning for Flexible Job-Shop Scheduling in PPVC Module Factories
Jul 13, 2026Ziheng Zhang, Wei ZhangManufacturing Reinforcement Learning
Singapore Institute of Technology, Singapore
Harmonizing Real-Time Constraints and Long-Horizon Reasoning: An Asynchronous Agentic Framework for Dynamic Scheduling
May 28, 2026Shijie Cao, Yuan Yuan, Jing LiuLanguage Model-Based Control Real-Time Scheduling
School of Computer Science and Engineering, Beihang University, Beijing 100191, China · Shenzhen Loop Area Institute, Shenzhen, China · Qingdao Research Institute, Beihang University +3
DynaSchedBench: Calibrated Dynamic Scheduling Benchmarks and Observability Paradox in LLM-based Scheduling Agents
May 26, 2026Shijie Cao, Yuan Yuan, Jing LiuLLM Agent Evaluation Combinatorial Optimization
School of Computer Science and Engineering, Beihang University, Beijing 100191, China · Shenzhen Loop Area Institute, Shenzhen, China · Qingdao Research Institute, Beihang University +3
Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals
May 21, 2026Yu Tang, Muhammad Zakwan, Efe Balta +2Reinforcement Learning Flexible Job Shop Scheduling
Centre for Artificial Intelligence, Zurich University of Applied Sciences, Switzerland · Inspire AG, Zurich, Switzerland · Automatic Control Laboratory, ETH Zurich, Switzerland
GOAL: Graph-based Objective-Aligned Diffusion Solvers for Dynamic Multi-Objective Optimization
May 18, 2026Xingyu LiMulti-Party Multi-Objective Optimization Multi-Objective Optimization
School of Engineering Technology Purdue University West Lafayette, IN 47907
Scheduling That Speaks: An Interpretable Programmatic Reinforcement Learning Framework
May 18, 2026Chengpeng Hu, Yingqian Zhang, Hendrik BaierReinforcement Learning Interpretable ML
Eindhoven University of Technology, Eindhoven, the Netherlands · Centrum Wiskunde & Informatica, Amsterdam, the Netherlands
Low-Cost Labels, Reliable Choices: Rollout-Calibrated Hyper-Heuristics for Job Shop Scheduling
May 11, 2026Junhao Wei, Yanxiao Li, Yifu Zhao +8Selective Prediction Metaheuristic Optimization
Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China · College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China · 4Macao Polytechnic University, Macao, 999078, China
An Analysis of the Coordination Gap between Joint and Modular Learning for Job Shop Scheduling with Transportation Resources
Apr 27, 2026Moritz Link, Jonathan Hoss, Noah KlarmannMulti-Agent Coordination Combinatorial Optimization
Faculty of Management and Engineering, University of Applied Sciences, 83024 Rosenheim, Germany
Scalable Production Scheduling: Linear Complexity via Unified Homogeneous Graphs
Apr 26, 2026Jonathan Hoss, Moritz Link, Noah KlarmannGraph Neural Networks Reinforcement Learning
Faculty of Management and Engineering, Rosenheim Technical University of Applied Sciences, Germany
Graph-RHO: Critical-path-aware Heterogeneous Graph Network for Long-Horizon Flexible Job-Shop Scheduling
Apr 11, 2026Yujie Li, Jiuniu Wang, Mugen Peng +2Flexible Job Shop Scheduling Job Shop Scheduling
Beijing University of Posts and Telecommunications Beijing, China · City University of Hong Kong Hong Kong, China · Chinese Academy of Sciences Beijing, China
TransGP: Task-Conditioned Transformer-Guided Genetic Programming for Multitask Dynamic Flexible Job Shop Scheduling
Apr 4, 2026Meng Xu, Jiao Liu, Hua Yu +1Job Shop Scheduling Automated Heuristic Design
RESCHED: Rethinking Flexible Job Shop Scheduling from a Transformer-based Architecture with Simplified States
Mar 7, 2026Xiangjie Xiao, Cong Zhang, Wen Song +1Flexible Job Shop Scheduling Job Shop Scheduling
School of Computing and Information Systems, Singapore Management University, Singapore · College of Computing and Data Science, Nanyang Technological University, Singapore · Institute of Marine Science and Technology, Shandong University, China
HGT-Scheduler: Deep Reinforcement Learning for the Job Shop Scheduling Problem via Heterogeneous Graph Transformers
Mar 6, 2026Bulent SoykanJob Shop Scheduling RL for Combinatorial Optimization
Institute for Simulation and Training, University of Central Florida, Orlando, FL, USA
Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling
Mar 3, 2026Jiaqi Wang, Zhiguang Cao, Peng Zhao +4Flexible Job Shop Scheduling Job Shop Scheduling
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University · College of Software, Jilin University · Singapore Management University +2
Learning Flexible Job Shop Scheduling under Limited Buffers and Material Kitting Constraints
Feb 27, 2026Shishun Zhang, Juzhan Xu, Yidan Fan +4Flexible Job Shop Scheduling Job Shop Scheduling
National University of Defense Technology · Shenzhen University · Institute of AI for Industries, Chinese Academy of Sciences
Mamba Meets Scheduling: Learning to Solve Flexible Job Shop Scheduling with Efficient Sequence Modeling
Feb 25, 2026Zhi Cao, Cong Zhang, Yaoxin Wu +2Flexible Job Shop Scheduling Job Shop Scheduling
Dalian University of Technology, Dalian, China · Nanyang Technological University, Singapore · Eindhoven University of Technology, Eindhoven, the Netherlands
Variational Approach for Job Shop Scheduling
Jan 30, 2026Seung Heon Oh, Jiwon Baek, Hyunjin Oh +3Job Shop Scheduling
Republic of Korea Navy · Department of Naval Architecture and Ocean Engineering Seoul National University · Research Institute of Marine Systems Engineering Seoul National University
A binarized-domains arc-consistency algorithm for TCSPs: its computational analysis and its use as a filtering procedure in solution search algorithms
Feb 22, 2020Amar IsliConstraint Satisfaction Temporal Consistency
University of Sciences and Technology Houari Boumedi`ene · Faculty of Computer Science · Department of Artiﬁcial Intelligence and Data Science +2

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APEX: An Extensible Model for Agent-Assisted Production Scheduling

Felix J. Grumbach, Stefan Görlitz

Qunevo GmbH, Germany

Multi-Objective Evolutionary Optimization Combinatorial Optimization Job Shop Scheduling

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Production scheduling requires realistic models that reflect operational constraints and efficient methods that balance competing goals. Putting these methods into use also requires data integration, model adaptation and specialist expertise. We present APEX, an extensible production scheduling framework built around a general model and hybrid multiobjective search. Agent assistance supports both scheduling and model refinement: agents prepare data and explore scenarios in natural language, while coding agents help implement and test new constraints and objectives. Shared construction and checking procedures connect these adaptations to the scheduling core. We benchmark eight APEX configurations against NSGA-II, SPEA2, MOEA/D and SMS-EMOA on 69 public job-shop, flexible job-shop and permutation flow-shop instances, assessing workload completion time (makespan), total job flowtime and computation time. Hybrid configurations achieve the best aggregate solution quality, although the leading method depends on the problem class and objective. A separate synthetic workflow study uses OpenAI's GPT-6-astra as an interaction layer between the human planner and the algorithmic core, testing rule additions, plan and objective changes, and what-if comparisons. All 24 sessions completed the requested changes and passed independent checks of saved models and schedules. A separate coding evaluation produced six native implementations of an additional objective or hard constraint through predefined extension hooks. All passed independent checks without modifying the core.