# Transformer-Based Flow Shop Scheduling Using MILP-Generated Training Data

arXiv: https://arxiv.org/abs/2608.29690 · HTML: https://mlsanity.com/paper/2608.29690/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 30 Aug 2026.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning](https://mlsanity.com/paper/2010.12367/) (2020)
- [PyGAD: An Intuitive Genetic Algorithm Python Library](https://mlsanity.com/paper/2106.06158/) (2021)
- [Self-Labeling the Job Shop Scheduling Problem](https://mlsanity.com/paper/2401.11849/) (2024)
- [Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method](https://mlsanity.com/paper/2110.06365/) (2021)
- [Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning](https://mlsanity.com/paper/2305.05119/) (2023)
- [Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning](https://mlsanity.com/paper/2210.17178/) (2022)
- [RESCHED: Rethinking Flexible Job Shop Scheduling from a Transformer-based Architecture with Simplified States](https://mlsanity.com/paper/2603.07020/) (2026)

## Abstract (arXiv)

Advances in machine learning (ML) have created new opportunities to complement traditional operations research (OR) methods. In particular, transformer models can capture complex interactions in token sequences by mapping tokens into a high-dimensional embedding space and propagating contextual information via attention. This makes them a candidate to model non-permutation flow shop scheduling with secondary resources as a next-token prediction task, where tokens represent job-machine-secondary resource tuples. For training, mixed-integer linear programming (MILP)-generated schedules are tokenized and used as next-token prediction data. During inference, partial token sequences (prefixes) are randomly generated and completed by the trained transformer through constrained decoding. A computational study is conducted on a flow shop with 8 jobs, 4 machines, and 3 secondary resources, where jobs are selected from a fixed pool of 20 jobs that is sampled during training and provides the candidates during prefix completion. The transformer achieves better solution quality (smaller makespans) compared to a genetic algorithm (GA), the NEH heuristic, and random search. It is outperformed only by the MILP model and the iterated greedy (IG) heuristic. The study concludes that transformer models can, to some extent, learn patterns from MILP-optimized non-permutation flow shop schedules and that transformer-based scheduling represents an interesting direction for future research, particularly in settings with a fixed, recurring job set.