# Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning

arXiv: https://arxiv.org/abs/2109.03323 · HTML: https://mlsanity.com/paper/2109.03323/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 67 references that are in the map.
- First posted on arXiv 7 Sep 2021.

## Built on by (most cited first, 3 of 3)

- [A Framework for Inherently Interpretable Optimization Models](https://mlsanity.com/paper/2208.12570/) (2022)
- [ReflecSched: Solving Dynamic Flexible Job-Shop Scheduling via LLM-Powered Hierarchical Reflection](https://mlsanity.com/paper/2508.01724/) (2025)
- [Feature-Based Interpretable Surrogates for Optimization](https://mlsanity.com/paper/2409.01869/) (2024)

## Abstract (arXiv)

The emergence of Industry 4.0 is making production systems more flexible and also more dynamic. In these settings, schedules often need to be adapted in real-time by dispatching rules. Although substantial progress was made until the '90s, the performance of these rules is still rather limited. The machine learning literature is developing a variety of methods to improve them, but the resulting rules are difficult to interpret and do not generalise well for a wide range of settings. This paper is the first major attempt at combining machine learning with domain problem reasoning for scheduling. The idea consists of using the insights obtained with the latter to guide the empirical search of the former. Our hypothesis is that this guided empirical learning process should result in dispatching rules that are effective and interpretable and which generalise well to different instance classes. We test our approach in the classical dynamic job shop scheduling problem minimising tardiness, which is one of the most well-studied scheduling problems. Nonetheless, results suggest that our approach was able to find new state-of-the-art rules, which significantly outperform the existing literature in the vast majority of settings, from loose to tight due dates and from low utilisation conditions to congested shops. Overall, the average improvement is 19%. Moreover, the rules are compact, interpretable, and generalise well to extreme, unseen scenarios.