# Quantum-Inspired DRL Approach with LSTM and OU Noise for Cut Order Planning Optimization

arXiv: https://arxiv.org/abs/2508.16611 · HTML: https://mlsanity.com/paper/2508.16611/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model](https://mlsanity.com/paper/2302.00244/) (2023)
- [Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance](https://mlsanity.com/paper/2206.03787/) (2022)
- [Machine Learning for Cutting Planes in Integer Programming: A Survey](https://mlsanity.com/paper/2302.09166/) (2023)
- [A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks](https://mlsanity.com/paper/2212.02721/) (2022)
- [Quantum-Inspired Machine Learning: a Survey](https://mlsanity.com/paper/2308.11269/) (2023)
- [Using Ornstein-Uhlenbeck Process to understand Denoising Diffusion Probabilistic Model and its Noise Schedules](https://mlsanity.com/paper/2311.17673/) (2023)
- [Solving the Order Batching and Sequencing Problem using Deep Reinforcement Learning](https://mlsanity.com/paper/2006.09507/) (2020)
- [Parameter estimation from an Ornstein-Uhlenbeck process with measurement noise](https://mlsanity.com/paper/2305.13498/) (2023)

## Abstract (arXiv)

Cut order planning (COP) is a critical challenge in the textile industry, directly impacting fabric utilization and production costs. Conventional methods based on static heuristics and catalog-based estimations often struggle to adapt to dynamic production environments, resulting in suboptimal solutions and increased waste. In response, we propose a novel Quantum-Inspired Deep Reinforcement Learning (QI-DRL) framework that integrates Long Short-Term Memory (LSTM) networks with Ornstein-Uhlenbeck noise. This hybrid approach is designed to explicitly address key research questions regarding the benefits of quantum-inspired probabilistic representations, the role of LSTM-based memory in capturing sequential dependencies, and the effectiveness of OU noise in facilitating smooth exploration and faster convergence. Extensive training over 1000 episodes demonstrates robust performance, with an average reward of 0.81 (-+0.03) and a steady decrease in prediction loss to 0.15 (-+0.02). A comparative analysis reveals that the proposed approach achieves fabric cost savings of up to 13% compared to conventional methods. Furthermore, statistical evaluations indicate low variability and stable convergence. Despite the fact that the simulation model makes several simplifying assumptions, these promising results underscore the potential of the scalable and adaptive framework to enhance manufacturing efficiency and pave the way for future innovations in COP optimization.