Title :

A deep reinforcement learning based multi-criteria decision support system for optimizing textile chemical process

Author(s) :

He, Zhenglei [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
École nationale supérieure des arts et industries textiles [ENSAIT]
Tran, Kim-Phuc [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
École nationale supérieure des arts et industries textiles [ENSAIT]
Thomassey, Sebastien [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
École nationale supérieure des arts et industries textiles [ENSAIT]
Zeng, Xianyi [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
École nationale supérieure des arts et industries textiles [ENSAIT]
Xu, J. [Auteur]
Yi, C. H. [Auteur]

Journal title :

Computers in Industry

Abbreviated title :

Comput. Ind.

Volume number :

125

Pages :

-

Publication date :

2021-03-09

ISSN :

0166-3615

English keyword(s) :

Deep reinforcement learning
Deep Q-Networks
Multi-criteria
Decision support
Process
Textile manufacturing

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
ENSAIT
Junia HEI

Collections :

• Génie des matériaux textiles (GEMTEX) - ULR 2641

Submission date :

2023-06-20T11:42:09Z
2024-03-21T08:22:59Z