Real-time information capturing and integration framework of the internet of manufacturing things

A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing

“…As the development of Internet of Things (IoT) and Cloud technologies, RFID twined with these technologies in the ubiquitous manufacturing is emerging. RFID identified production resources were converted into smart manufacturing objects (SMOs) which are able to sense, behave, and interact adaptively within the ubiquitous environment for achieving intelligent manufacturing with advanced decision models [10,11]. Decision models are changed within the RFID-enabled real-time ubiquitous manufacturing environment.…”

A Timed Colored Petri Net Simulation-Based Self-Adaptive Collaboration Method for Production-Logistics Systems

“…A token in this place represents a production task p 2 A token in this place represents a worker p 3 A token in this place represents a material p 4 A token in this place represents the attendance of the worker p 5 A token in this place represents enough materials p 6 A token in this place represents the absence of the worker p 7 A token in this place represents insufficient materials p 8 A token in this place represents the sataus information of a machine p 9 A token in this place represents the sataus information of an AGV p 10 A token in this place represents the finish of the machine processing p 11 A token in this place represents the request to the logistics task p 12 A token in this place represents the detection result p 13 A token in this place represents the qualified WIP p 14 A token in this place represents the nearest and available AGV p 15 A token in this place represents the unqualified WIP p 16 A token in this place represents the remote or unavailable AGV p 17 A token in this place represents an out-buffer p 18 A token in this place represents the selected logistics task p 19 A token in this place represents the correct start point p 20 A token in this place represents the transport time p 21 A token in this place represents the transport to the destination p 22 A token in this place represents the correct destination p 23 A token in this place represents the end and next cycle Figure 3 shows the TCPN model of the self-adaptive collaboration method. This model refers to the basic cycle of production-logistics systems, which consists of twenty-three places and fourteen transitions.…”

“…The transition aims at modeling the check of the worker t 2 The transition aims at modeling the check of the material t 3 The transition aims at modeling requesting the worker t 4 The transition aims at modeling requesting the material t 5 The transition aims at modeling the machine processing and publishing a logistics task t 6 The transition aims at modeling the quality detection t 7 The transition aims at modeling the selection of the AGV t 8 The transition aims at modeling the temporary storage t 9 The transition aims at modeling the selected AGV going to the start point t 10 The transition aims at modeling the reprocessing or the scarp t 11 The transition aims at modeling the rejection of the AGV t 12 The transition aims at modeling picking pallet and loading t 13 The transition aims at modeling the selected AGV going to the destination t 14 The transition aims at modeling the unloading Table 2. Places in Figure 3.…”

Methods and Tools for Performance Assurance of Smart Manufacturing Systems

“…Some of the main challenges to leveraging the above trends in technology are inconsistent information quality and lingering interoperability limitations [58,59]. SMS applications are composed of different components and heterogeneous technologies.…”