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

TL;DR

This paper introduces a quantum-inspired deep reinforcement learning framework with LSTM and OU noise to optimize cut order planning in textiles, achieving significant cost savings and stable convergence.

Contribution

It presents a novel hybrid QI-DRL approach combining quantum-inspired probabilistic models, LSTM memory, and OU noise for dynamic manufacturing optimization.

Findings

Achieves up to 13% fabric cost savings.

Demonstrates robust performance over 1000 training episodes.

Shows stable convergence with low variability.

Abstract

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.