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Adapting Petri Nets to Discrete Event Simulation for the Stochastic Modelling of Manufacturing Systems

Simon, E, Oyekan, J, Hutabarat, W, Tiwari, A and Turner, C J (2018) Adapting Petri Nets to Discrete Event Simulation for the Stochastic Modelling of Manufacturing Systems International Journal of Simulation Modelling, 17 (1). pp. 5-17.

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Discrete - Event Simulation (DES) is commonly used for the simulation of manufacturing systems. In many practical cases, DES practitioners ha ve to make simplifications or to use the software in an unconventional or convoluted fashion to meet their needs. Petri nets enable the development of transparent models which allow increased flexibility and control for designers. Furthermore, Petri nets t ake advantage of a solid mathematical ground and constitute a simple language. However, Petri nets lack the software capabilities to realise their full potential. This study investigates the suitability and relevance of Discrete - Event Simulation (DES) soft ware for Petri net modelling in the context of manufacturing systems. A framework is developed for the modelling of different classes of Petri nets on DES. Analytical models of asynchronous flow lines are developed. Initial results show that the analytical models are without closed - form solution and the explosion of the state space is observed, justifying the use of computational methods and simulation for the analysis of manufacturing systems. This study shows that the gain in flexibility provided by Petri nets provides a new insight into the effects of stochasticity on setup and failure times in manufacturing systems.

Item Type: Article
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
Simon, E
Oyekan, J
Hutabarat, W
Tiwari, A
Turner, C
Date : March 2018
Funders : EPSRC
DOI : 10.2507/IJSIMM17(1)403
Copyright Disclaimer : Copyright ©2018 DAAAM International Vienna, All Rights Reserved. Posted here with kind permission of the publisher.
Uncontrolled Keywords : Petri N et , Discrete - E vent S imulation , Stochastic M odelling , Manufacturing Plant Layout
Depositing User : Melanie Hughes
Date Deposited : 19 Jul 2018 11:43
Last Modified : 11 Dec 2018 11:24

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