Journal
IEEE ACCESS
Volume 10, Issue -, Pages 42983-43002Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3168000
Keywords
Internet of Things; Manufacturing; Analytical models; Petri nets; Stochastic processes; Production; Process control; Formal verification; colored generalized stochastic Petri net; timed and stochastic process; process manufacturing model; BPMN
Categories
Funding
- Postdoctoral Fellowship (Ratchadaphiseksomphot Endowment Fund) of Chulalongkorn University
Ask authors/readers for more resources
This paper proposes a quantitative verification approach for analyzing and optimizing IoT manufacturing design models. The approach combines the use of BPMN and CGSPN representations to help designers identify and fix issues in manufacturing models.
Internet of Things (IoT) technologies have been increasingly developed for real-time application in manufacturing processes to address heterogeneous devices and software effectively. Although almost all activities in a manufacturing process can perform an action when data objects arrive at the activity, physical devices or activities have process involving the operation of chance over time and probabilistic function for proceeding with their operations. Therefore, the formal verification of an IoT process design model have to consider the timed constraints, probabilistic tasks and dependencies between activities. This paper proposes a quantitative verification approach for analyzing and optimizing IoT manufacturing design models that are designed in business process model and notation (BPMN) representation. The transformation rules of BPMN element into the colored generalized stochastic Petri net (CGSPN) are proposed, and the stepwise approaches for refining and verifying the components of the CGSPN models are illustrated. Our framework helps the designers to automate the CGSPN model and to localize the operational gaps, time and flaws of the process manufacturing models.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available