A big-data-driven matching model based on deep reinforcement learning for cotton blending
针对中国棉纺织业数字化转型中成本与质量平衡的难题，提出一种基于深度强化学习的大数据驱动配棉匹配模型，利用交易、交互和测量数据设计奖励值，通过离线策略和记忆库迭代优化纱线配棉方案，在保证质量的前提下降低制造成本。
China’s cotton textile industry is undergoing a critical period of digital transformation and upgrading to cope with pressure and challenges such as rising labour costs and large fluctuations in raw material prices. Developing a cost-based competitive advantage while ensuring a high-quality product is a critical problem in intelligent manufacturing. From the perspective of big data and reinforcement learning, the authors designed a reward value combining transaction, interaction, and measurement data by combining the reward mechanism and Markov decision for a combination of different raw materials in the intelligent textile factory. The authors propose a big data-driven application to the depth of the reinforcement learning to solve problems and build a big-data-driven matching model based on deep reinforcement learning to cotton matching. The offline strategy is designed to construct a memory bank and neural network, and the incentive mechanism of reinforcement learning is used to iterate the optimal yarn matching scheme to achieve the goal of intelligent cotton matching. The results show that deep reinforcement learning can be optimised using big data on the premise of quality assurance. Manufacturing costs can be optimised using a matching model of big data based on a deep reinforcement learning model.