# Variational Approach for Job Shop Scheduling

arXiv: https://arxiv.org/abs/2602.00408 · HTML: https://mlsanity.com/paper/2602.00408/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 16 of its 62 references that are in the map.
- First posted on arXiv 30 Jan 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 16 of 16)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Autoencoding beyond pixels using a learned similarity metric](https://mlsanity.com/paper/1512.09300/) (2015)
- [Ladder Variational Autoencoders](https://mlsanity.com/paper/1602.02282/) (2016)
- [Decoupling Representation Learning from Reinforcement Learning](https://mlsanity.com/paper/2009.08319/) (2020)
- [Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model](https://mlsanity.com/paper/1907.00953/) (2019)
- [DARLA: Improving Zero-Shot Transfer in Reinforcement Learning](https://mlsanity.com/paper/1707.08475/) (2017)
- [Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning](https://mlsanity.com/paper/2010.12367/) (2020)
- [Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning](https://mlsanity.com/paper/2106.01086/) (2021)
- [ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning](https://mlsanity.com/paper/2106.03051/) (2021)
- [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)
- [Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problem](https://mlsanity.com/paper/2309.15517/) (2023)
- [Automatic programming via large language models with population self-evolution for dynamic fuzzy job shop scheduling problem](https://mlsanity.com/paper/2410.22657/) (2024)
- [Offline reinforcement learning for job-shop scheduling problems](https://mlsanity.com/paper/2410.15714/) (2024)
- [Learning-enabled Flexible Job-shop Scheduling for Scalable Smart Manufacturing](https://mlsanity.com/paper/2402.08979/) (2024)

## Abstract (arXiv)

This paper proposes a novel Variational Graph-to-Scheduler (VG2S) framework for solving the Job Shop Scheduling Problem (JSSP), a critical task in manufacturing that directly impacts operational efficiency and resource utilization. Conventional Deep Reinforcement Learning (DRL) approaches often face challenges such as non-stationarity during training and limited generalization to unseen problem instances because they optimize representation learning and policy execution simultaneously. To address these issues, we introduce variational inference to the JSSP domain for the first time and derive a probabilistic objective based on the Evidence of Lower Bound (ELBO) with maximum entropy reinforcement learning. By mathematically decoupling representation learning from policy optimization, the VG2S framework enables the agent to learn robust structural representations of scheduling instances through a variational graph encoder. This approach significantly enhances training stability and robustness against hyperparameter variations. Extensive experiments demonstrate that the proposed method exhibits superior zero-shot generalization compared with state-of-the-art DRL baselines and traditional dispatching rules, particularly on large-scale and challenging benchmark instances such as DMU and SWV.