  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.
