Textile manufacturing is a typical traditional industry involving high complexity in interconnected processes with limited capacity on the application of modern technologies. Decision-making in this domain generally takes multiple criteria into consideration, which usually arouses more complexity. To address this issue, the present paper proposes a decision support system that combines the intelligent data-based models of random forest (RF) and a human knowledge-based multi-criteria structure of analytical hierarchical process (AHP) in accordance with the objective and the subjective factors of the textile manufacturing process. More importantly, the textile chemical manufacturing process is described as the Markov decision process (MDP) paradigm, and a deep reinforcement learning scheme, the Deep Q-networks (DQN), is employed to optimize it. The effectiveness of this system has been validated in a case study of optimizing a textile ozonation process, showing that it can better master the challenging decision-making tasks in textile chemical manufacturing processes.

纺织制造业是典型的传统行业，在相互连接的过程中涉及高度复杂性，现代技术的应用能力有限。该领域的决策通常考虑多个标准，这通常会引起更多的复杂性。为了解决这个问题，本文提出了一种决策支持系统，根据目标和目标，将基于智能数据的随机森林（RF）模型和基于人类知识的分析层次过程（AHP）的多准则结构相结合。纺织制造过程的主观因素。更重要的是，纺织化学制造过程被描述为马尔可夫决策过程（MDP）范式，并采用了深度强化学习方案Deep Q-networks（DQN）对其进行优化。