# A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints

arXiv: https://arxiv.org/abs/2506.13566 · HTML: https://mlsanity.com/paper/2506.13566/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 16 Jun 2025.

## 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, 3 of 3)

- [A Reinforcement Learning Environment For Job-Shop Scheduling](https://mlsanity.com/paper/2104.03760/) (2021)
- [Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods](https://mlsanity.com/paper/2308.12794/) (2023)
- [Optimizing Job Shop Scheduling in the Furniture Industry: A Reinforcement Learning Approach Considering Machine Setup, Batch Variability, and Intralogistics](https://mlsanity.com/paper/2409.11820/) (2024)

## Built on by (most cited first, 2 of 2)

- [When Does Hierarchy Help? Benchmarking Agent Coordination in Event-Driven Industrial Scheduling](https://mlsanity.com/paper/2605.13172/) (2026)
- [Bridging the Sim-to-Real Gap in Reinforcement Learning-Based Industrial Dispatching through Execution Semantics](https://mlsanity.com/paper/2605.29078/) (2026)

## Abstract (arXiv)

The classical Job Shop Scheduling Problem (JSSP) focuses on optimizing makespan under deterministic constraints. Real-world production environments introduce additional complexities that cause traditional scheduling approaches to be less effective. Reinforcement learning (RL) holds potential in addressing these challenges, as it allows agents to learn adaptive scheduling strategies. However, there is a lack of a comprehensive, general-purpose frameworks for effectively training and evaluating RL agents under real-world constraints. To address this gap, we propose a modular framework that extends classical JSSP formulations by incorporating key real-world constraints inherent to the shopfloor, including transport logistics, buffer management, machine breakdowns, setup times, and stochastic processing conditions, while also supporting multi-objective optimization. The framework is a customizable solution that offers flexibility in defining problem instances and configuring simulation parameters, enabling adaptation to diverse production scenarios. A standardized interface ensures compatibility with various RL approaches, providing a robust environment for training RL agents and facilitating the standardized comparison of different scheduling methods under dynamic and uncertain conditions. We release JobShopLab as an open-source tool for both research and industrial applications, accessible at: https://github.com/proto-lab-ro/jobshoplab