Usability of Deterministic and Stochastic Petri Nets in the Wood Industry: A Case Study

Horváth, Ádám

doi:10.1007/978-3-319-06569-4_9

Cited by 2 publications

(2 citation statements)

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“…DSPNs are of limited use in modelling production systems because an analytical solution of steady-state can only be had with DSPNs if there is no more than one deterministic transition enabled in any marking. [15]…”

Section: Related Workmentioning

confidence: 99%

Dynamic production system identification for smart manufacturing systems

Denno

Dickerson

Harding

2018

Journal of Manufacturing Systems

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This paper presents a methodology, called production system identification, to produce a model of a manufacturing system from logs of the system’s operation. The model produced is intended to aid in making production scheduling decisions. Production system identification is similar to machine-learning methods of process mining in that they both use logs of operations. However, process mining falls short of addressing important requirements; process mining does not (1) account for infrequent exceptional events that may provide insight into system capabilities and reliability, (2) offer means to validate the model relative to an understanding of causes, and (3) updated the model as the situation on the production floor changes. The paper describes a genetic programming (GP) methodology that uses Petri nets, probabilistic neural nets, and a causal model of production system dynamics to address these shortcomings. A coloured Petri net formalism appropriate to GP is developed and used to interpret the log. Interpreted logs provide a relation between Petri net states and exceptional system states that can be learned by means of novel formulation of probabilistic neural nets (PNNs). A generalized stochastic Petri net and the PNNs are used to validate the GP-generated solutions. The methodology is evaluated with an example based on an automotive assembly system.

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Section: Related Workmentioning

confidence: 99%

Dynamic production system identification for smart manufacturing systems

Denno

Dickerson

Harding

2018

Journal of Manufacturing Systems

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“…They applied their method at a Dutch logistics provider and showed that it was more accurate than earlier methods. Horváth used deterministic and stochastic Petri nets (DSPN) to analyse the production at a Hungarian window manufacturing company and to determine the throughput and bottleneck of the process [76]. Wan et al built stochastic Petri net models of an integrated modular avionics (IMA) architecture and studied its health monitoring and fault management scheme [77].…”

Section: Related Work -Petri Nets In Industrial Case Studiesmentioning

confidence: 99%

Industrial applications of the PetriDotNet modelling and analysis tool

Vörös

Darvas

Hajdu

et al. 2018

Science of Computer Programming

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Since their invention, Petri nets have provided modelling and analysis methods to support the design of correct, reliable and robust systems. This motivated our work to develop PetriDotNet, a Petri net editor and analysis tool. In this paper we overview the supported modelling formalisms and the analysis methods included in PetriDotNet. Next, we present eight different industrial case studies, demonstrating the wide variety of scenarios where Petri nets and PetriDotNet can help the design, development and analysis of industrial systems. Our original goal with PetriDotNet was to provide an educational tool to our students, however our efforts led to a framework being able to serve both academic and industrial needs.

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Usability of Deterministic and Stochastic Petri Nets in the Wood Industry: A Case Study

Cited by 2 publications

References 1 publication

Dynamic production system identification for smart manufacturing systems

Dynamic production system identification for smart manufacturing systems

Industrial applications of the PetriDotNet modelling and analysis tool

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