# Learning to generalize Dispatching rules on the Job Shop Scheduling

arXiv: https://arxiv.org/abs/2206.04423 · HTML: https://mlsanity.com/paper/2206.04423/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 9 Jun 2022.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [POMO: Policy Optimization with Multiple Optima for Reinforcement Learning](https://mlsanity.com/paper/2010.16011/) (2020)
- [Reinforcement Learning for Solving the Vehicle Routing Problem](https://mlsanity.com/paper/1802.04240/) (2018)
- [An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem](https://mlsanity.com/paper/1906.01227/) (2019)
- [Order Matters: Sequence to sequence for sets](https://mlsanity.com/paper/1511.06391/) (2015)
- [Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning](https://mlsanity.com/paper/2010.12367/) (2020)
- [Learning Scheduling Algorithms for Data Processing Clusters](https://mlsanity.com/paper/1810.01963/) (2018)
- [Evaluating Curriculum Learning Strategies in Neural Combinatorial Optimization](https://mlsanity.com/paper/2011.06188/) (2020)
- [Constrained Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/2006.11984/) (2020)
- [Manufacturing Dispatching using Reinforcement and Transfer Learning](https://mlsanity.com/paper/1910.02035/) (2019)

## Built on by (most cited first, 7 of 7)

- [Self-Labeling the Job Shop Scheduling Problem](https://mlsanity.com/paper/2401.11849/) (2024)
- [Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling](https://mlsanity.com/paper/2211.10936/) (2022)
- [Graph Neural Networks for Job Shop Scheduling Problems: A Survey](https://mlsanity.com/paper/2406.14096/) (2024)
- [Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem](https://mlsanity.com/paper/2402.17606/) (2024)
- [schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments](https://mlsanity.com/paper/2301.04182/) (2023)
- [Curriculum Learning in Job Shop Scheduling using Reinforcement Learning](https://mlsanity.com/paper/2305.10192/) (2023)
- [Curriculum Learning with GNN-based Reinforcement Learning for Job Shop Scheduling](https://mlsanity.com/paper/2609.28085/) (2026)

## Abstract (arXiv)

This paper introduces a Reinforcement Learning approach to better generalize heuristic dispatching rules on the Job-shop Scheduling Problem (JSP). Current models on the JSP do not focus on generalization, although, as we show in this work, this is key to learning better heuristics on the problem. A well-known technique to improve generalization is to learn on increasingly complex instances using Curriculum Learning (CL). However, as many works in the literature indicate, this technique might suffer from catastrophic forgetting when transferring the learned skills between different problem sizes. To address this issue, we introduce a novel Adversarial Curriculum Learning (ACL) strategy, which dynamically adjusts the difficulty level during the learning process to revisit the worst-performing instances. This work also presents a deep learning model to solve the JSP, which is equivariant w.r.t. the job definition and size-agnostic. Conducted experiments on Taillard's and Demirkol's instances show that the presented approach significantly improves the current state-of-the-art models on the JSP. It reduces the average optimality gap from 19.35% to 10.46% on Taillard's instances and from 38.43% to 18.85% on Demirkol's instances. Our implementation is available online.