Advanced manufacturing systems: supply–demand matching of manufacturing resource based on complex networks and Internet of Things

ABSTRACT

After investigation on the existing advanced manufacturing systems (AMSs), it is found that supply–demand matching of manufacturing resource is one of the common issues to be addressed in all AMSs, and methods for addressing this issue have evolved from P2P (peer-to-peer)-based, to information centre-based, and to platform (or system)-based matching, and are moving towards socialisation and service-based solutions. In order to adapt to this trend, a new method for manufacturing resource supply–demand matching based on complex networks and Internet of Things (IoT) is proposed, and a four-layered architecture for implementing this method is designed. In this method, IoT technology is employed to realise the intelligent perception and accessing of various manufacturing resources and capabilities (MR&C), which enables logical aggregation of various distributed MR&C in the form of services. Then complex networks model and theory are used to realise the efficient manufacturing service management, optimal-allocation, and supply–demand matching. In this article, the specific key technologies for implementing the method are presented, including key technologies for manufacturing service generation and aggregation, manufacturing demand/task management, supply–demand matching of MR&C in the form of services, and value/utility adding based on manufacturing service network (MSN), manufacturing task network (MTN) and manufacturing enterprises collaborative network (ECN).

KEYWORDS:

• Advanced manufacturing systems (AMSs)
• supply–demand matching
• Internet of Tings (IoT)
• complex network
• enterprises collaborative network (ECN)
• hypernetwork

Acknowledgements

This work is partly supported by National Natural Science Foundation of China (51475032), Beijing Natural Science Foundation (Grant 4152032), National High-Tech. R&D Program of China (Grant 2015AA042100), and the Innovation Foundation of BUAA for PhD Graduates.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was partly supported by National Natural Science Foundation of China [grant number 51475032], Beijing Natural Science Foundation [grant number 4152032], National High-Tech. R&D Program of China [grant number 2015AA042100], and the Innovation Foundation of BUAA for PhD Graduates.