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A big-data-driven matching model based on deep reinforcement learning for cotton blending
,
Justin Zuopeng Zhang ,
Andrew Thomas
International Journal of Production Research, Volume: 61, Issue: 22, Pages: 7573 - 7591
Swansea University Author:
Andrew Thomas
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DOI (Published version): 10.1080/00207543.2022.2153942
Abstract
China’s cotton textile industry is undergoing a critical period of digital transformation and upgrading to cope with pressure and challenges such as rising labour costs and large fluctuations in raw material prices. Developing a cost-based competitive advantage while ensuring a high-quality product...
| Published in: | International Journal of Production Research |
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| ISSN: | 0020-7543 1366-588X |
| Published: |
Informa UK Limited
2023
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa66945 |
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| Abstract: |
China’s cotton textile industry is undergoing a critical period of digital transformation and upgrading to cope with pressure and challenges such as rising labour costs and large fluctuations in raw material prices. Developing a cost-based competitive advantage while ensuring a high-quality product is a critical problem in intelligent manufacturing. From the perspective of big data and reinforcement learning, the authors designed a reward value combining transaction, interaction, and measurement data by combining the reward mechanism and Markov decision for a combination of different raw materials in the intelligent textile factory. The authors propose a big data-driven application to the depth of the reinforcement learning to solve problems and build a big-data-driven matching model based on deep reinforcement learning to cotton matching. The offline strategy is designed to construct a memory bank and neural network, and the incentive mechanism of reinforcement learning is used to iterate the optimal yarn matching scheme to achieve the goal of intelligent cotton matching. The results show that deep reinforcement learning can be optimised using big data on the premise of quality assurance. Manufacturing costs can be optimised using a matching model of big data based on a deep reinforcement learning model. |
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| Keywords: |
Deep reinforcement learning; big data-driven; reinforcement learningreward mechanism design; cotton blending costoptimisation; matchingmodel |
| College: |
Faculty of Humanities and Social Sciences |
| Funders: |
This research has been supported by the National Natural Sci-ence Foundation of China (NSFC: 71871172, title: Model ofrisk knowledge acquisition and platform governance in FinTechbased on deep learning; NSFC: 72171184, title: Grey privateknowledge model of security and trusted BI on the federallearning perspective). We sincerely appreciate the suggestionsfrom fellow members of Xia’s project team and the ResearchCenter of Enterprise Decision Support, Key Research Insti-tute of Humanities and Social Sciences in Universities of HubeiProvince (DSS2021). |
| Issue: |
22 |
| Start Page: |
7573 |
| End Page: |
7591 |