Adaptive production strategy in vertical farm digital twins with Q-learning algorithms
Abstract Urban food production can contribute to sustainable development goals by reducing land use and shortening transportation distances. Despite its advantages, the implementation of digital twin (DT) technology for urban food systems has received less investigation compared to manufacturing. Th...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-97123-y |
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| author | Yujia Luo Peter Ball |
| author_facet | Yujia Luo Peter Ball |
| author_sort | Yujia Luo |
| collection | DOAJ |
| description | Abstract Urban food production can contribute to sustainable development goals by reducing land use and shortening transportation distances. Despite its advantages, the implementation of digital twin (DT) technology for urban food systems has received less investigation compared to manufacturing. This article examines the influence of DT technology on adaptive decision-making in urban food production, focusing on the “Grow It York” case study. Utilising mixed integer linear programming (MILP) and Q-learning models, this study explores how DT data enhances production decisions regarding service level and resource utilisation under demand fluctuations. The findings highlight that the Q-learning model achieves up to $$78.5\%$$ demand fulfillment compared to $$58.5\%$$ for the MILP model, demonstrating a significant improvement in operational efficiency. Additionally, electricity usage per fulfilled demand is reduced by approximately $$15\%$$ , advocating for broader DT application to the synergy between economic resilience and environmental sustainability. Future research directions include scaling DT implementation to manage complex supply chains, including advancing real-time data integration and incorporating sustainability considerations at supply chain level. |
| format | Article |
| id | doaj-art-d9bb96860a9a4ffdb9d7ccb3cdcdd77f |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d9bb96860a9a4ffdb9d7ccb3cdcdd77f2025-08-20T02:10:49ZengNature PortfolioScientific Reports2045-23222025-04-0115111410.1038/s41598-025-97123-yAdaptive production strategy in vertical farm digital twins with Q-learning algorithmsYujia Luo0Peter Ball1School of Business and Society, The University of YorkSchool of Business and Society, The University of YorkAbstract Urban food production can contribute to sustainable development goals by reducing land use and shortening transportation distances. Despite its advantages, the implementation of digital twin (DT) technology for urban food systems has received less investigation compared to manufacturing. This article examines the influence of DT technology on adaptive decision-making in urban food production, focusing on the “Grow It York” case study. Utilising mixed integer linear programming (MILP) and Q-learning models, this study explores how DT data enhances production decisions regarding service level and resource utilisation under demand fluctuations. The findings highlight that the Q-learning model achieves up to $$78.5\%$$ demand fulfillment compared to $$58.5\%$$ for the MILP model, demonstrating a significant improvement in operational efficiency. Additionally, electricity usage per fulfilled demand is reduced by approximately $$15\%$$ , advocating for broader DT application to the synergy between economic resilience and environmental sustainability. Future research directions include scaling DT implementation to manage complex supply chains, including advancing real-time data integration and incorporating sustainability considerations at supply chain level.https://doi.org/10.1038/s41598-025-97123-yDigital twinUrban food systemAdaptive production strategiesQ-learning network |
| spellingShingle | Yujia Luo Peter Ball Adaptive production strategy in vertical farm digital twins with Q-learning algorithms Scientific Reports Digital twin Urban food system Adaptive production strategies Q-learning network |
| title | Adaptive production strategy in vertical farm digital twins with Q-learning algorithms |
| title_full | Adaptive production strategy in vertical farm digital twins with Q-learning algorithms |
| title_fullStr | Adaptive production strategy in vertical farm digital twins with Q-learning algorithms |
| title_full_unstemmed | Adaptive production strategy in vertical farm digital twins with Q-learning algorithms |
| title_short | Adaptive production strategy in vertical farm digital twins with Q-learning algorithms |
| title_sort | adaptive production strategy in vertical farm digital twins with q learning algorithms |
| topic | Digital twin Urban food system Adaptive production strategies Q-learning network |
| url | https://doi.org/10.1038/s41598-025-97123-y |
| work_keys_str_mv | AT yujialuo adaptiveproductionstrategyinverticalfarmdigitaltwinswithqlearningalgorithms AT peterball adaptiveproductionstrategyinverticalfarmdigitaltwinswithqlearningalgorithms |