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Volume 6 Issue 4
Jul.  2019

IEEE/CAA Journal of Automatica Sinica

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Jiani Zhou, Jiacun Wang and Jun Wang, "A Simulation Engine for Stochastic Timed Petri Nets and Application to Emergency Healthcare Systems," IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 969-980, July 2019. doi: 10.1109/JAS.2019.1911576
Citation: Jiani Zhou, Jiacun Wang and Jun Wang, "A Simulation Engine for Stochastic Timed Petri Nets and Application to Emergency Healthcare Systems," IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 969-980, July 2019. doi: 10.1109/JAS.2019.1911576

A Simulation Engine for Stochastic Timed Petri Nets and Application to Emergency Healthcare Systems

doi: 10.1109/JAS.2019.1911576
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  • In many service delivery systems, the quantity of available resources is often a decisive factor of service quality. Resources can be personnel, offices, devices, supplies, and so on, depending on the nature of the services a system provides. Although service computing has been an active research topic for decades, general approaches that assess the impact of resource provisioning on service quality matrices in a rigorous way remain to be seen. Petri nets have been a popular formalism for modeling systems exhibiting behaviors of competition and concurrency for almost a half century. Stochastic timed Petri nets (STPN), an extension to regular Petri nets, are a powerful tool for system performance evaluation. However, we did not find any single existing STPN software tool that supports all timed transition firing policies and server types, not to mention resource provisioning and requirement analysis. This paper presents a generic and resource oriented STPN simulation engine that provides all critical features necessary for the analysis of service delivery system quality vs. resource provisioning. The power of the simulation system is illustrated by an application to emergency health care systems.

     

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    Highlights

    • The simulation tool supports single-server and multiple-server(infinite-server) transition models.
    • The simulation tool supports pre-selection and race transition firing policies.
    • The simulation is GUI-driven and supports both step-by-step mode and batch execution mode.
    • The simulation tool support resource provisioning.

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