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A big-data-driven matching model based on deep reinforcement learning for cotton blending

Huosong Xia, Yuan Wang, Sajjad Jasimuddin Orcid Logo, Justin Zuopeng Zhang Orcid Logo, Andrew Thomas Orcid Logo

International Journal of Production Research, Volume: 61, Issue: 22, Pages: 7573 - 7591

Swansea University Author: Andrew Thomas Orcid Logo

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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...

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Published in: International Journal of Production Research
ISSN: 0020-7543 1366-588X
Published: Informa UK Limited 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa66945
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last_indexed 2024-11-25T14:19:13Z
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spelling 2024-09-13T13:04:05.3517934 v2 66945 2024-07-04 A big-data-driven matching model based on deep reinforcement learning for cotton blending 13d5ed33bce79c052f678401128e4ca1 0000-0002-1942-7050 Andrew Thomas Andrew Thomas true false 2024-07-04 CBAE 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. Journal Article International Journal of Production Research 61 22 7573 7591 Informa UK Limited 0020-7543 1366-588X Deep reinforcement learning; big data-driven; reinforcement learningreward mechanism design; cotton blending costoptimisation; matchingmodel 17 11 2023 2023-11-17 10.1080/00207543.2022.2153942 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University Another institution paid the OA fee 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). 2024-09-13T13:04:05.3517934 2024-07-04T14:33:01.7818891 Faculty of Humanities and Social Sciences School of Management - Business Management Huosong Xia 1 Yuan Wang 2 Sajjad Jasimuddin 0000-0003-2627-9241 3 Justin Zuopeng Zhang 0000-0002-4074-9505 4 Andrew Thomas 0000-0002-1942-7050 5 66945__31320__fc4b198bb4f645e7aa4a321d572ffbf7.pdf 66945.VoR.pdf 2024-09-13T13:00:37.7724775 Output 4037680 application/pdf Version of Record true © 2022 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License. true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title A big-data-driven matching model based on deep reinforcement learning for cotton blending
spellingShingle A big-data-driven matching model based on deep reinforcement learning for cotton blending
Andrew Thomas
title_short A big-data-driven matching model based on deep reinforcement learning for cotton blending
title_full A big-data-driven matching model based on deep reinforcement learning for cotton blending
title_fullStr A big-data-driven matching model based on deep reinforcement learning for cotton blending
title_full_unstemmed A big-data-driven matching model based on deep reinforcement learning for cotton blending
title_sort A big-data-driven matching model based on deep reinforcement learning for cotton blending
author_id_str_mv 13d5ed33bce79c052f678401128e4ca1
author_id_fullname_str_mv 13d5ed33bce79c052f678401128e4ca1_***_Andrew Thomas
author Andrew Thomas
author2 Huosong Xia
Yuan Wang
Sajjad Jasimuddin
Justin Zuopeng Zhang
Andrew Thomas
format Journal article
container_title International Journal of Production Research
container_volume 61
container_issue 22
container_start_page 7573
publishDate 2023
institution Swansea University
issn 0020-7543
1366-588X
doi_str_mv 10.1080/00207543.2022.2153942
publisher Informa UK Limited
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description 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.
published_date 2023-11-17T17:30:16Z
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score 11.08899

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