论文标题
纺织制造过程优化的基于深入的增强学习的多标准决策支持系统
A Deep Reinforcement Learning Based Multi-Criteria Decision Support System for Textile Manufacturing Process Optimization
论文作者
论文摘要
纺织品制造是一个典型的传统行业，涉及在现代技术应用能力有限的互连过程中的高复杂性。该领域的决策通常会考虑多个标准，这通常会引起更复杂的性能。为了解决这个问题，本文提出了一个决策支持系统，该系统结合了基于数据的随机森林（RF）模型和基于人类知识的分析分层过程（AHP）多标准结构，该结构是根据纺织品制造过程的目标和主观因素。更重要的是，纺织品制造过程被描述为马尔可夫决策过程（MDP）范式，并且采用了深入的加固学习方案，即深Q-networks（DQN）来优化它。该系统的有效性已在优化纺织臭氧过程的案例研究中得到了验证，表明它可以更好地掌握纺织品制造过程中具有挑战性的决策任务。
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 random forest (RF) models and a human knowledge based analytical hierarchical process (AHP) multi-criteria structure in accordance to the objective and the subjective factors of the textile manufacturing process. More importantly, the textile 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 manufacturing processes.
