# Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning

arXiv: https://arxiv.org/abs/2305.05119 · HTML: https://mlsanity.com/paper/2305.05119/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 55 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [Reinforcement Learning for Solving the Vehicle Routing Problem](https://mlsanity.com/paper/1802.04240/) (2018)
- [Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning](https://mlsanity.com/paper/2010.12367/) (2020)
- [Convolutional Networks with Dense Connectivity](https://mlsanity.com/paper/2001.02394/) (2020)
- [Matrix Encoding Networks for Neural Combinatorial Optimization](https://mlsanity.com/paper/2106.11113/) (2021)
- [Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning](https://mlsanity.com/paper/2106.01086/) (2021)
- [Learning ReLU Networks on Linearly Separable Data: Algorithm, Optimality, and Generalization](https://mlsanity.com/paper/1808.04685/) (2018)

## Built on by (most cited first, 20 of 20)

- [Graph Neural Networks for Job Shop Scheduling Problems: A Survey](https://mlsanity.com/paper/2406.14096/) (2024)
- [Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions](https://mlsanity.com/paper/2310.03195/) (2023)
- [Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop Scheduling](https://mlsanity.com/paper/2502.15791/) (2025)
- [Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling](https://mlsanity.com/paper/2409.10589/) (2024)
- [A Unified Framework for Combinatorial Optimization Based on Graph Neural Networks](https://mlsanity.com/paper/2406.13125/) (2024)
- [ReflecSched: Solving Dynamic Flexible Job-Shop Scheduling via LLM-Powered Hierarchical Reflection](https://mlsanity.com/paper/2508.01724/) (2025)
- [Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem](https://mlsanity.com/paper/2403.09249/) (2024)
- [Solving the flexible job-shop scheduling problem through an enhanced deep reinforcement learning approach](https://mlsanity.com/paper/2310.15706/) (2023)
- [Scalable Production Scheduling: Linear Complexity via Unified Homogeneous Graphs](https://mlsanity.com/paper/2604.23841/) (2026)
- [Offline reinforcement learning for job-shop scheduling problems](https://mlsanity.com/paper/2410.15714/) (2024)
- [ReLA: Representation Learning and Aggregation for Job Scheduling with Reinforcement Learning](https://mlsanity.com/paper/2601.03646/) (2026)
- [A Learning Method with Gap-Aware Generation for Heterogeneous DAG Scheduling](https://mlsanity.com/paper/2603.23249/) (2026)
- [An efficient deep reinforcement learning environment for flexible job-shop scheduling](https://mlsanity.com/paper/2509.07019/) (2025)
- [Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions](https://mlsanity.com/paper/2509.10303/) (2025)
- [Multi-Action Self-Improvement for Neural Combinatorial Optimization](https://mlsanity.com/paper/2510.12273/) (2025)
- [Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling](https://mlsanity.com/paper/2603.02846/) (2026)
- [Graph-RHO: Critical-path-aware Heterogeneous Graph Network for Long-Horizon Flexible Job-Shop Scheduling](https://mlsanity.com/paper/2604.10073/) (2026)
- [A Sliding-Window-Based Reinforcement Learning for Dynamic Assembly Flow Shop Scheduling with Multi-Product Delivery](https://mlsanity.com/paper/2607.02941/) (2026)
- [Time-Lag-Aware Deep Reinforcement Learning for Flexible Job-Shop Scheduling in PPVC Module Factories](https://mlsanity.com/paper/2607.11725/) (2026)
- [Transformer-Based Flow Shop Scheduling Using MILP-Generated Training Data](https://mlsanity.com/paper/2608.29690/) (2026)

## Abstract (arXiv)

Flexible manufacturing has given rise to complex scheduling problems such as the flexible job shop scheduling problem (FJSP). In FJSP, operations can be processed on multiple machines, leading to intricate relationships between operations and machines. Recent works have employed deep reinforcement learning (DRL) to learn priority dispatching rules (PDRs) for solving FJSP. However, the quality of solutions still has room for improvement relative to that by the exact methods such as OR-Tools. To address this issue, this paper presents a novel end-to-end learning framework that weds the merits of self-attention models for deep feature extraction and DRL for scalable decision-making. The complex relationships between operations and machines are represented precisely and concisely, for which a dual-attention network (DAN) comprising several interconnected operation message attention blocks and machine message attention blocks is proposed. The DAN exploits the complicated relationships to construct production-adaptive operation and machine features to support high-quality decisionmaking. Experimental results using synthetic data as well as public benchmarks corroborate that the proposed approach outperforms both traditional PDRs and the state-of-the-art DRL method. Moreover, it achieves results comparable to exact methods in certain cases and demonstrates favorable generalization ability to large-scale and real-world unseen FJSP tasks.