# Manufacturing Dispatching using Reinforcement and Transfer Learning

arXiv: https://arxiv.org/abs/1910.02035 · HTML: https://mlsanity.com/paper/1910.02035/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2019.

## Built on by (most cited first, 4 of 4)

- [Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions](https://mlsanity.com/paper/2310.03195/) (2023)
- [Learning to generalize Dispatching rules on the Job Shop Scheduling](https://mlsanity.com/paper/2206.04423/) (2022)
- [Multi-level Explanation of Deep Reinforcement Learning-based Scheduling](https://mlsanity.com/paper/2209.09645/) (2022)
- [Offline Reinforcement Learning for Warehouse SLAM Throughput Control](https://mlsanity.com/paper/2606.23978/) (2026)

## Abstract (arXiv)

Efficient dispatching rule in manufacturing industry is key to ensure product on-time delivery and minimum past-due and inventory cost. Manufacturing, especially in the developed world, is moving towards on-demand manufacturing meaning a high mix, low volume product mix. This requires efficient dispatching that can work in dynamic and stochastic environments, meaning it allows for quick response to new orders received and can work over a disparate set of shop floor settings. In this paper we address this problem of dispatching in manufacturing. Using reinforcement learning (RL), we propose a new design to formulate the shop floor state as a 2-D matrix, incorporate job slack time into state representation, and design lateness and tardiness rewards function for dispatching purpose. However, maintaining a separate RL model for each production line on a manufacturing shop floor is costly and often infeasible. To address this, we enhance our deep RL model with an approach for dispatching policy transfer. This increases policy generalization and saves time and cost for model training and data collection. Experiments show that: (1) our approach performs the best in terms of total discounted reward and average lateness, tardiness, (2) the proposed policy transfer approach reduces training time and increases policy generalization.