Adaptive production strategy in vertical farm digital twins with Q-learning algorithms

TY - JOUR

T1 - Adaptive production strategy in vertical farm digital twins with Q-learning algorithms

AU - Luo, Yujia

AU - Ball, Peter David

N1 - © The Author(s) 2025

PY - 2025/4/30

Y1 - 2025/4/30

N2 - 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 farms 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 levels and resource utilisation under demand uncertainties. The findings highlight DT potential to significantly improve operational efficiency and robustness, advocating for its broader application in scaled-up food production systems to boost economic resilience and environmental sustainability. Future research directions include scaling these models to manage complex supply chain (SC) data, such as advanced data integration, and incorporating environmental and social impact considerations.

AB - 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 farms 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 levels and resource utilisation under demand uncertainties. The findings highlight DT potential to significantly improve operational efficiency and robustness, advocating for its broader application in scaled-up food production systems to boost economic resilience and environmental sustainability. Future research directions include scaling these models to manage complex supply chain (SC) data, such as advanced data integration, and incorporating environmental and social impact considerations.

U2 - 10.1038/s41598-025-97123-y

DO - 10.1038/s41598-025-97123-y

M3 - Article

SN - 2045-2322

VL - 15

JO - Scientific Reports

JF - Scientific Reports

M1 - 15129

ER -