A Deep Reinforcement Learning Based Multi-Criteria Decision Support System for Textile Manufacturing Process Optimization

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.Comment: arXiv admin note: text overlap with arXiv:2012.0110

Markov Decision Process in the Problem of Dynamic Pricing Policy

Markov decision processes (MDP) are widely used in problems whose solutions may be represented by a certain series of actions. A lot of papers demonstrate successful MDP use in model problems, robotic control problems, planning problems, etc. In addition, economic problems have the property of multistep motion towards a goal as well. This paper is dedicated to MDP application to the problem of pricing policy management. The problem of dynamic pricing is stated in terms of MDP. Additional attention is paid to the method of constructing an MDP model based on data mining. Based on the data on sales of an actual industrial plant, construction of an MDP model that includes the searching for and generalization of regularities is demnstrated