TY - JOUR
T1 - Operation twins: production-intralogistics synchronisation in Industry 4.0
AU - Li, Mingxing
AU - Guo, Daqiang
AU - Li, Ming
AU - Qu, Ting
AU - Huang, George Q.
N1 - This work was supported by several funding sources, including National Natural Science Foundation of China (No. 52005218, 51875251), the 2019 Guangdong Special Support Talent Program – Innovation and Entrepreneurship Leading Team (China) (2019BT02S593), and 2018 Guangzhou Leading Innovation Team Program (201909010006), National Key Research and Development Program of China (2021YFB3301701), and the Science and Technology Development Fund (Macau SAR) (0078/2021/A).
Publisher Copyright:
© 2022 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2022
Y1 - 2022
N2 - The widespread adoption of Industry 4.0 technologies is revolutionising how manufacturing operations are managed and done. This revolution drives manufacturing practitioners to reevaluate their current manufacturing planning and control (MPC) strategies to maintain global competitiveness. The production and intralogistics (PiL) operations within traditional MPC systems are organised separately, which results in inferior overall solutions. PiL operations in a single factory are inherently coupled and interact with each other throughout the entire process, which needs synchronous organisation and operations. This paper introduces a novel concept of operations twins (OT), with vertical twinning and horizontal twining, for achieving PiL synchronisation by leveraging Industry 4.0 technologies and innovative operations management strategies. An Internet-of-Things (IoT)-based vertical twinning method is developed for real-time object-level data collection and information-sharing between PiL. A horizontal twinning mechanism is proposed to support real-time coordination of production and intralogistics operations with real-time information-sharing. A numerical study is carried out, and the results show that OT outperforms the widely used static and dynamic methods regarding the overall stability and typical measures such as makespan, average manufacturing time, and average tardiness under different levels of uncertainties.
AB - The widespread adoption of Industry 4.0 technologies is revolutionising how manufacturing operations are managed and done. This revolution drives manufacturing practitioners to reevaluate their current manufacturing planning and control (MPC) strategies to maintain global competitiveness. The production and intralogistics (PiL) operations within traditional MPC systems are organised separately, which results in inferior overall solutions. PiL operations in a single factory are inherently coupled and interact with each other throughout the entire process, which needs synchronous organisation and operations. This paper introduces a novel concept of operations twins (OT), with vertical twinning and horizontal twining, for achieving PiL synchronisation by leveraging Industry 4.0 technologies and innovative operations management strategies. An Internet-of-Things (IoT)-based vertical twinning method is developed for real-time object-level data collection and information-sharing between PiL. A horizontal twinning mechanism is proposed to support real-time coordination of production and intralogistics operations with real-time information-sharing. A numerical study is carried out, and the results show that OT outperforms the widely used static and dynamic methods regarding the overall stability and typical measures such as makespan, average manufacturing time, and average tardiness under different levels of uncertainties.
KW - Industry 4.0
KW - manufacturing planning and control
KW - operations twins
KW - production and intralogistics
KW - real-time information-sharing
KW - synchronisation
UR - http://www.scopus.com/inward/record.url?scp=85134167918&partnerID=8YFLogxK
U2 - 10.1080/00207543.2022.2098874
DO - 10.1080/00207543.2022.2098874
M3 - Journal article
AN - SCOPUS:85134167918
SN - 0020-7543
JO - International Journal of Production Research
JF - International Journal of Production Research
ER -