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Digital twin data-driven proactive job-shop scheduling strategy towards asymmetric manufacturing execution decision
The information asymmetry phenomenon widely exists in production management decisions due to the latency of manufacturing data transmissions. Also, stochastic events on the physical production site will result in information asymmetry, which may lead to inconsistency between current execution and pr...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8799727/ https://www.ncbi.nlm.nih.gov/pubmed/35091567 http://dx.doi.org/10.1038/s41598-022-05304-w |
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author | Zhang, Fuqiang Bai, Junyan Yang, Dongyu Wang, Qiang |
author_facet | Zhang, Fuqiang Bai, Junyan Yang, Dongyu Wang, Qiang |
author_sort | Zhang, Fuqiang |
collection | PubMed |
description | The information asymmetry phenomenon widely exists in production management decisions due to the latency of manufacturing data transmissions. Also, stochastic events on the physical production site will result in information asymmetry, which may lead to inconsistency between current execution and previous resource allocation plans. It is meaningful and important for developing an information model based on the Internet of Manufacturing Things to timely and actively adjust the scheduling strategy to meet the symmetry requirements of the production execution process. Based on the digital twin data collected from the workshop, a proactive job-shop scheduling strategy was discussed in this paper. Firstly, the mechanism for the influence of delayed local operations on makespan was deduced. Then, a framework for implementing the proactive job-shop scheduling strategy was proposed. Coordination point was used to determine the adjustment interval of local operations; right-shift rule with delay time constraints was used to adjust the unprocessed operation sequences on machines. Finally, the examples including 6*6 (6 jobs, 6 machines) and 20*40 (20 jobs, 40 machines) were presented to verify the effectiveness and scalability of the proposed method. It can be predicted that the proactive scheduling strategy provides the online decisions for the efficient and smooth execution of the digital twin-driven workshop production. |
format | Online Article Text |
id | pubmed-8799727 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-87997272022-02-01 Digital twin data-driven proactive job-shop scheduling strategy towards asymmetric manufacturing execution decision Zhang, Fuqiang Bai, Junyan Yang, Dongyu Wang, Qiang Sci Rep Article The information asymmetry phenomenon widely exists in production management decisions due to the latency of manufacturing data transmissions. Also, stochastic events on the physical production site will result in information asymmetry, which may lead to inconsistency between current execution and previous resource allocation plans. It is meaningful and important for developing an information model based on the Internet of Manufacturing Things to timely and actively adjust the scheduling strategy to meet the symmetry requirements of the production execution process. Based on the digital twin data collected from the workshop, a proactive job-shop scheduling strategy was discussed in this paper. Firstly, the mechanism for the influence of delayed local operations on makespan was deduced. Then, a framework for implementing the proactive job-shop scheduling strategy was proposed. Coordination point was used to determine the adjustment interval of local operations; right-shift rule with delay time constraints was used to adjust the unprocessed operation sequences on machines. Finally, the examples including 6*6 (6 jobs, 6 machines) and 20*40 (20 jobs, 40 machines) were presented to verify the effectiveness and scalability of the proposed method. It can be predicted that the proactive scheduling strategy provides the online decisions for the efficient and smooth execution of the digital twin-driven workshop production. Nature Publishing Group UK 2022-01-28 /pmc/articles/PMC8799727/ /pubmed/35091567 http://dx.doi.org/10.1038/s41598-022-05304-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhang, Fuqiang Bai, Junyan Yang, Dongyu Wang, Qiang Digital twin data-driven proactive job-shop scheduling strategy towards asymmetric manufacturing execution decision |
title | Digital twin data-driven proactive job-shop scheduling strategy towards asymmetric manufacturing execution decision |
title_full | Digital twin data-driven proactive job-shop scheduling strategy towards asymmetric manufacturing execution decision |
title_fullStr | Digital twin data-driven proactive job-shop scheduling strategy towards asymmetric manufacturing execution decision |
title_full_unstemmed | Digital twin data-driven proactive job-shop scheduling strategy towards asymmetric manufacturing execution decision |
title_short | Digital twin data-driven proactive job-shop scheduling strategy towards asymmetric manufacturing execution decision |
title_sort | digital twin data-driven proactive job-shop scheduling strategy towards asymmetric manufacturing execution decision |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8799727/ https://www.ncbi.nlm.nih.gov/pubmed/35091567 http://dx.doi.org/10.1038/s41598-022-05304-w |
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