Phase-type approximation of stochastic Petri nets for analysis of manufacturing systems

2000 ◽  
Vol 16 (3) ◽  
pp. 318-322 ◽  
Author(s):  
Shang-Tae Yee ◽  
J.A. Ventura
Keyword(s):  
Petri Nets ◽  
Manufacturing Systems ◽  
Stochastic Petri Nets ◽  
Type Approximation ◽  
Phase Type

Modeling and Analyzing the Material Flow of Crowdsourcing Processes in Cloud-Based Manufacturing Systems Using Stochastic Petri Nets

2014 ◽  
Author(s):  
Dazhong Wu ◽  
David W. Rosen ◽  
Dirk Schaefer
Keyword(s):  
Petri Nets ◽  
Material Flow ◽  
Manufacturing Systems ◽  
Cloud Manufacturing ◽  
Stochastic Petri Nets ◽  
Manufacturing Processes ◽  
Manufacturing Enterprises ◽  
Car Suspension ◽  
Manufacturing Performance

Cloud-based manufacturing (CBM), also referred to as cloud manufacturing, has the potential to allow manufacturing enterprises to be rapidly scaled up and down by crowdsourcing manufacturing tasks or sub-tasks. To improve the efficiency of the crowdsourcing process, the material flow of CBM systems needs to be managed so that several manufacturing processes can be executed simultaneously. Further, the scalability of manufacturing capacity in CBM needs to be designed, analyzed, and planned in response to rapidly changing market demands. The objective of this paper is to introduce a stochastic petri nets (SPNs)-based approach for modeling and analyzing the concurrency and synchronization of the material flow in CBM systems. The proposed approach is validated through a case study of a car suspension module. Our results have shown that the SPN-based approach helps analyze the structural and behavioral properties of a CBM system and verify manufacturing performance.

Extended knowledge attributed Petri Nets

2014 ◽  
Vol 05 (02) ◽  
pp. 1350028
Author(s):  
Attila Fűr
Keyword(s):  
Petri Nets ◽  
Intelligent Agents ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Object Oriented ◽  
Physical Reality ◽  
Knowledge Bases ◽  
Stochastic Petri Nets ◽  
Inference Engines ◽  
Soft Computing Techniques

Choosing the best way for describing physical reality has always been standing in focus of research. Several methodologies have been developed based on classical mathematics, or statistics and also new disciplines — such as soft-computing techniques — appeared. Petri Nets as one of the most naturalistic modeling methodologies are well suited to describe complex process in general. However in some fields of modeling the describing power of basic Petri Nets proved not to be robust enough, therefore several extensions were made to the original concept. Colored tokens (Colored Petri Nets), stochastic delayed streaming of mobile entities (Stochastic Petri Nets), object oriented architecture (Object Oriented Petri Nets), numerical (Numerical Petri Nets) and linguistic attributes (Fuzzy Petri Nets) broaden the range of capabilities. In some fields of problem solving, usage of static and mobile knowledge bases is needed: e.g., flexible manufacturing systems, or intelligent traffic simulation. These problems to be investigated involved new conceptual developments of Petri Nets and led to the introduction of Knowledge Attributed Petri Nets. At the same time distributed control in simulation appeared, intelligent agents supported the connection of mobile knowledge bases and static inference engines in an effective way. The mentioned extensions brought general support in model synthesis, but some unsolved questions remained related to the implementation of intelligent mobile entities. This paper highlights a new level of AI controlled simulation introducing the Extended Knowledge Attributed Petri Nets that offer the capability of easy implementation of mobile inference engines and knowledge base, providing general mobile AI in Petri Nets.

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