TY - JOUR
T1 - Spatial-Temporal Finite Element Analytics for Cyber-Physical System-enabled Smart Factory: Application in Hybrid Flow Shop
AU - Li, Mingxing
AU - Jiang, Min
AU - Lyu, Zhongyuan
AU - Chen, Qiqi
AU - Wu, Haoye
AU - Huang, George Q.
N1 - Funding Information:
Acknowledgement to Zhejiang Provincial, Hangzhou Municipal, Lin'an City Governments, Hong Kong ITF Innovation and Technology Support Program (ITP/079/16LP) and partial financial support from the 2019 Guangdong Special Support Talent Program – Innovation and Entrepreneurship Leading Team (China) (2019BT02S593).
Publisher Copyright:
© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021.
PY - 2021/6
Y1 - 2021/6
N2 - Cyber-Physical System (CPS) is a crucial direction in realizing the next-generation smart manufacturing. With the deployment of IoT technologies, massive production data and information are real-timely accessible for decision-makers. However, the enemies: complexity and uncertainty, that researchers and practitioners have been fighting for decades still hinder the development of smart manufacturing. In the realm of manufacturing optimization like production planning and scheduling, considerable research efforts have been made. However, more or less similar results are generated, which are theoretically optimal but lack the ability to cope with disturbances in the real-life industry. This study develops a framework of the Cyber-Physical System (CPS) and proposes a smart digitalization solution to achieve real-time visibility, traceability, and information sharing. Also, a novel Spatial-Temporal Finite Element Analytics (ST-FEA) based on real-time cyber-physical visibility and traceability is innovated to achieve real-time advanced production planning and scheduling in the Hybrid Flow Shop. By discretizing the space and time of the original problem in meshing, the complexity and uncertainty are substantially reduced. The simpler models can thus be established and solved with a more straightforward method. Case studies will be conducted to verify the practicability and effectiveness of the proposed method in future research.
AB - Cyber-Physical System (CPS) is a crucial direction in realizing the next-generation smart manufacturing. With the deployment of IoT technologies, massive production data and information are real-timely accessible for decision-makers. However, the enemies: complexity and uncertainty, that researchers and practitioners have been fighting for decades still hinder the development of smart manufacturing. In the realm of manufacturing optimization like production planning and scheduling, considerable research efforts have been made. However, more or less similar results are generated, which are theoretically optimal but lack the ability to cope with disturbances in the real-life industry. This study develops a framework of the Cyber-Physical System (CPS) and proposes a smart digitalization solution to achieve real-time visibility, traceability, and information sharing. Also, a novel Spatial-Temporal Finite Element Analytics (ST-FEA) based on real-time cyber-physical visibility and traceability is innovated to achieve real-time advanced production planning and scheduling in the Hybrid Flow Shop. By discretizing the space and time of the original problem in meshing, the complexity and uncertainty are substantially reduced. The simpler models can thus be established and solved with a more straightforward method. Case studies will be conducted to verify the practicability and effectiveness of the proposed method in future research.
KW - Advanced Planning
KW - Cyber-Physical System (CPS)
KW - Smart manufacturing
KW - Spatial-Temporal Finite Element Analytics (ST-FEA)
KW - Synchronization
KW - Scheduling (APS)
UR - http://www.scopus.com/inward/record.url?scp=85098684653&partnerID=8YFLogxK
U2 - 10.1016/j.promfg.2020.10.172
DO - 10.1016/j.promfg.2020.10.172
M3 - Conference article
AN - SCOPUS:85098684653
SN - 2351-9789
VL - 51
SP - 1229
EP - 1236
JO - Procedia Manufacturing
JF - Procedia Manufacturing
T2 - 30th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2021
Y2 - 15 June 2021 through 18 June 2021
ER -