Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics

Logistics

Shahryar Sorooshian¹  Veera Pandiyan Kaliani Sundram²  Jaafar Pyeman²  Ahmed Zainul Abideen²  Abdul Kadir Othman² 

[1] Department of Business Administration, University of Gothenburg, 41124 Gothenburg, Sweden;Institute of Business Excellence, Universiti Teknologi MARA, Shah Alam 40450, Malaysia;

关键词: digital twin;    data-driven technology;    lean manufacturing;    supply chain 4.0;    reinforced learning;    simulation modelling;   

DOI  :  10.3390/logistics5040084

来源: DOAJ

【 摘 要 】

Background: As the Internet of Things (IoT) has become more prevalent in recent years, digital twins have attracted a lot of attention. A digital twin is a virtual representation that replicates a physical object or process over a period of time. These tools directly assist in reducing the manufacturing and supply chain lead time to produce a lean, flexible, and smart production and supply chain setting. Recently, reinforced machine learning has been introduced in production and logistics systems to build prescriptive decision support platforms to create a combination of lean, smart, and agile production setup. Therefore, there is a need to cumulatively arrange and systematize the past research done in this area to get a better understanding of the current trend and future research directions from the perspective of Industry 4.0. Methods: Strict keyword selection, search strategy, and exclusion criteria were applied in the Scopus database (2010 to 2021) to systematize the literature. Results: The findings are snowballed as a systematic review and later the final data set has been conducted to understand the intensity and relevance of research work done in different subsections related to the context of the research agenda proposed. Conclusion: A framework for data-driven digital twin generation and reinforced learning has been proposed at the end of the paper along with a research paradigm.

【 授权许可】

Unknown