Data-driven cloud simulation architecture for automated flexible production lines: application in real smart factories

Abstract

In recent years, more manufacturing enterprises are building automated flexible production lines (AFPLs) to satisfy the dynamic and diversified demand. Currently, static planning methods can hardly meet the requirements of the dynamic resource allocation for AFPLs. The technologies of the digital twin can help solve dynamic problems. Therefore, we propose a data-driven cloud simulation architecture for AFPLs in smart factories. First, we design a cloud simulation platform as the architecture foundation. Second, we use the data-driven modelling and simulation method to achieve automated modelling. Third, we implement the system on the cloud using Java, MySQL, and the Anylogic platform, and verify the efficiency of the proposed method by experiments in the real workshop of a 3C (Computer, Communication, Consumer electronics) company. The experimental results show the proposed architecture can support the real-time resource allocation decisions to maximise the throughput in AFPLs. This paper makes contributions by proposing an architecture realising automatic modelling and data-driven simulation first in the cloud simulation environment, and filling the gap of dynamic resource allocation in the research of AFPLs.

KEYWORDS:

• Smart manufacturing
• data driven
• cloud computing
• digital twin
• automated flexible production line
• dynamic resource allocation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was partially supported by NSFC Key Project (Grant No. 71620107002), NSSFC Major Project (Grant No. 16ZDA013), NKRDPC (Grant No. 2018YFB1702700), CPSF (Grant No. 2019M652665), YPNNSFC (Grant No. 51705379 & 51905196), and NNSFCG (Grant No.71620107002). Yeming Gong is supported by Business Intelligence Center (BIC) and AIM Institute of EMLYON.

Notes on contributors

Dan Luo

Dan Luo received the B.S. degree in Logistics Engineering from the school of Logistics Engineering, Wuhan University of Technology, Wuhan, China, in 2015 and M.S. degree in industrial engineering from the school of Mechanical Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, China, in 2017. Her research interests include modelling and simulation of manufacturing system and logistics analysis systems, big data-driven logistics and production management, cloud simulation platform, and automated flexible production line of smart factories.

Zailin Guan

Zailin Guan is a Professor in Department of Industrial Engineering, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China. He holds a Ph.D. from Huazhong University of Science and Technology, Wuhan, China. He was a post-doc researcher at Hong Kong University of Science and Technology. Prof. Guan has presided over two projects of National Natural Science Foundation and one project of 863 programme. He has participated in a programme jointly funded by National Natural Science Foundation and Hong Kong Research Grants Council and a programme of EU FP6 cooperation. He published 30 articles in journals like International Journal of Production Research, Journal of Intelligent Manufacturing, Engineering Optimization, and Journal of Manufacturing Systems. His research interests include advance planning and scheduling systems, constraints management, supply chain management, logistics and line balancing.

Cong He

Cong He is graduated as a Ph.D. in mechanical engineering from Huazhong University of Science and Technology (HUST), Wuhan, China. His research interests include intelligent manufacturing, optimisation, planning and scheduling, line balancing, and automated flexible production line of smart factories.

Yeming Gong

Yeming Gong is a Professor of Management Science at EMLYON Business School, France. He is Head and AIM Institute and Director of Business Intelligence Center. He holds a Ph.D. from Rotterdam School of Management, Erasmus University, Netherlands. He has published two books ‘Stochastic Modelling and Analysis of Warehouse Operations’ in Erasmus and ‘Global Operations Strategy: Fundamentals and Practice’ in Springer. He published 80 articles in journals like International Journal of Production Research, Production and Operations Management, IIE Transactions, Transportation Science, European Journal of Operational Research, International Journal of Production Economics, and IEEE Transactions on Engineering Management. Prof. Gong received ‘2010 the Best Paper Award in Design and Manufacturing’ from IIE, and ‘Erasmus Scholarship for Teaching’ from European Union.

Lei Yue

Lei Yue is graduated as a Ph.D. in industrial engineering from Huazhong University of Science and Technology (HUST), Wuhan, China in 2017. He was also Post Doctorate researcher at the School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China from January 2018 to March 2021. Currently, he is an Associate Professor at Guangzhou University, Guangzhou, China. His research interests include intelligent manufacturing, optimisation, planning and scheduling, supply chain management, reverse logistics, line balancing.