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

Jiani Zhou; Jiacun Wang; Jun Wang

doi:10.1109/JAS.2019.1911576

Volume 6 Issue 4

Jul. 2019

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2019 > 6(4): 969-980

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

Citation:

PDF( 1198 KB)

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

doi: 10.1109/JAS.2019.1911576

More Information

Author Bio:
Jiani Zhou received the B.E. and M.E. degrees in automation science from Nanjing University of Science and Technology, China, in 2016 and 2019, respectively, and the M.S. degree in software engineering from Monmouth University, USA, in 2019. Her research interests include Petri Nets and automation

Jiacun Wang received the Ph.D. degree in computer engineering from Nanjing University of Science and Technology (NUST), China, in 1991. He is currently a Professor of software engineering at Monmouth University, West Long Branch, New Jersey, USA. From 2001 to 2004, he was a member of scientific staff with Nortel Networks in Richardson, Texas. Prior to joining Nortel, he was a Research Associate of the School of Computer Science, Florida International University (FIU) at Miami. Prior to joining FIU, he was an Associate Professor at NUST. His research interests include software engineering, discrete event systems, formal methods, wireless networking, and real-time distributed systems. He authored Timed Petri Nets: Theory and Application (Kluwer, 1998), Real-time Embedded Systems (Wiley, 2018) and Formal Methods in Computer Science (CRC, 2019), edited Handbook of Finite Stat Based Models and Applications (CRC, 2012), and published about 90 research papers in journals and conferences. He was an Associate Editor of IEEE Transactions on Systems, Man, and Cybernetics, Part C. He has served as general chair, program chair, and special sessions chair or program committee member for many international conferences

Jun Wang received the Ph.D. degree in automation science from Nanjing University of Science and Technology (NJUST), China, in 2009. He is currently an Associate Professor at NJUST. His research interests include control theory and application, embedded software development and application, control system integration, etc. He has published 32 academic articles and authored one book. He also won two provincial-level research prizes
Corresponding author: J. N. Zhou and J. C. Wang are with Monmouth University, New Jersey 07764-1898 USA (e-mail: s1217842@monmouth.edu; jwang@monmouth.edu)
Received Date: 2019-02-01
Revised Date: 2019-04-07
Accepted Date: 2019-04-29

Available Online: 2019-06-04

Abstract

Abstract

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.
- Emergency department,
- healthcare,
- resource requirements,
- simulation, sojourn time,
- stochastic timed Petri nets (STPNs),
- workflows

FullText(HTML)

References(34)

References

[1]	Y. Liu, H. Zhang, C. Li, and R. Jiao, " Workflow simulation for operational decision support using event graph through process mining,” Decision Support Systems, vol. 52, pp. 685–697, 2012. doi: 10.1016/j.dss.2011.11.003
[2]	J. Wang and D. Li, " Resource oriented workflow nets and workflow resource requirement analysis,” Int. J. Software Engineering and Knowledge Engineering, vol. 23, no. 5, pp. 667–693, 2013.
[3]	T. Murata, " Petri nets: properties, analysis and applications,” in Proc. IEEE, vol. 77, no. 4, pp. 541–580, 1989. doi: 10.1109/5.24143
[4]	N. Wu and M. Zhou, " Intelligent token Petri nets for modelling and control of reconfigurable automated manufacturing systems with dynamical changes,” Trans. Institute of Measurement and Control, vol. 33, no. 1, pp. 9–29, 2009.
[5]	M. Zhou and N. Wu, System Modeling and Control with Resource-Oriented Petri Nets, CRC Press, 2009.
[6]	G. Liu, " Complexity of the deadlock problem for Petri nets modeling resource allocation systems, information sciences—informatics and computer science,” Applications:An Int. J., vol. 363, no. C, pp. 190–197, 2017.
[7]	J. Wang, Timed Petri Nets: Theory and Application, Kluwer Academic Publishers, 1998.
[8]	M. K. Molloy, " On the integration of delay and throughput measures in distributed processing models,” Ph.D. dissertation, UCLA, Los Angeles, CA, 1981.
[9]	F. J. W. " Symons. modeling and analysis of communication protocols using numerical petri nets,” Ph.D. dissertation, University of Essex, 1978.
[10]	M. A. Marsan, G. Balbo, G. Conte, S. Donatelli, and G. Franceschinis. Modelling with Generalized Stochastic Petri Nets. John Wiley and Sons, 1995.
[11]	J. Dugan, K. S. Trivedi, R. Geist, and V. Nicola, " Extended stochastic Petri nets: applications and analysis,” in Proc. Performance, Paris, France, Dec. 1984.
[12]	G. Ciardo and C. Lindemann, " Analysis of deterministic and stochastic petri nets,” in Proc. Petri Nets and Performance Models, 1993.
[13]	M. A. Marsan and G. Chiola, " On Petri nets with deterministic and exponentially distributed firing times,” Advances in Petri Nets, pp. 132–145. Springer Berlin Heidelberg, 1987.
[14]	Z. Huang and J. Wang, " Hybrid state analysis for non-Markovian stochastic Petri net models,” Chinese J. Automation, vol. 7, no. 1, 1995.
[15]	G. Horton, " A new paradigm for the numerical simulation of stochastic petri nets with general firing times,” in Proc. European Simulation Symposium, pp. 129–136, 2002.
[16]	S. Lazarova-Molnar and G. Horton, " Proxel-Based Simulation of stochastic Petri nets containing immediate transitions,” Electronic Notes in Theoretical Computer Science, vol. 85, no. 4, pp. 203–217, 2003.
[17]	W. D. Kelton and A. M. Law. Simulation Modeling and Analysis. McGraw Hill Boston, MA, 2000.
[18]	List of Petri net tools on the web page of Department of Informatics, University of Hamburg, Germany. [Online]. Available: http://www.informatik.uni-hamburg.de/TGI/PetriNets/tools/complete_db.html.
[19]	S. Baarir, M. Beccuti, D. Cerotti, M. De Pierro, S. Donatelli, and G. Franceschinis, " The GreatSPN tool: recent enhancements,” ACM SIGMETRICS Performance Evaluation Review, vol. 36, no. 4, pp. 4–9, 2009. doi: 10.1145/1530873
[20]	E. Gilberto and S. Donatelli, " Dspn-tool: a new DSPN and GSPN solver for greatspn,” in 7th Int. Conf. Quantitative Evaluation of Systems, pp. 79–80, Sep. 2010.
[21]	A. Zimmermann and M. Knoke, TimeNET 4.0: A software Tool for the Performability Evaluation with Stochastic and Colored Petri Nets; User Manual. TU, Professoren der Fak. IV, 2007.
[22]	P. Bonet, C. M. Llad´o, R. Puijaner, and W. J. Knottenbelt, " Pipe v2.5: A P Boucheneb1, Petri net tool for performance modelling,” in Proc. 23rd Latin American Conference on Informatics, 2007.
[23]	C. Hirel, B. Tuffin, and K. S. Trivedi, " SPNP: stochastic Petri nets. version 6.0,” in Computer Performance Evaluation. Modelling Techniques and Tools, pp. 354–357. Springer, 2000.
[24]	S. Kounev, S. Spinner, and P. Meier, " QPME 2.0 — a tool for stochastic modeling and analysis using queueing Petri nets,” in From Active Data Management to Event-based Systems and More, pp. 293–311. Springer, 2010.
[25]	Á. Horváth and A. Molnár, TiPeNeSS: A Timed Petri Net Simulator Software with Generally Distributed Firing Delays, SIMUTOOLS 2015, Athens, Greece, 2015
[26]	M. Dotoli, M. P. Fanti, G. Iacobellis, L. Martino, A. M. Moretti, and W. UKovich, " Modeling and management of a hospital department via petri nets,” in Proc. IEEE Workshop on Health Care Management, 2010.
[27]	M. Fanti, A. Mangni, M. Dotoli, and W. Ukovich, " A three-level strategy for the design and performance evaluation of hospital departments,” IEEE Trans. Systems, Man and Cybernetics, Systems: 43(4):742–756, 2013.
[28]	C. C. Mahulea, L. Mahulea, J. García-Soriano, and J. Colom, " Modular Petri net modeling of healthcare systems. flexible services and manufacturing journal, special issue on analysis,” Design and Management of Health Care Systems, 2017.
[29]	J. Wang, J. Tian, and R Sun, " Emergency healthcare resource requirement analysis: a stochastic timed Petri net Approach,” in IEEE 15th Int. Conf. Networking, Sensing and Control, IEEE, Mar. 2018.
[30]	G. Liu, Z. Li, and A. Al- Ahmni, " Liveness Analysis of Petri nets using siphons and mathematical programming,” IFAC Proceedings, vol. 47, no. 2, pp. 383–387, 2014. doi: 10.3182/20140514-3-FR-4046.00078
[31]	H. Boucheneb, D. Lime, and O. H. Roux, " On Multi-enabledness in time Petri nets,” in Proc. Application and Theory of Petri Nets and Concurrency, pp 130–149, 2013.
[32]	M. Boyer and M. Diaz, " Multiple enabledness of transitions in Petri nets with time, ” in Proc. 9th Int. Workshop Petri Nets and Performance Models, Aachen, Germany, Sep. 2001.
[33]	G. Liu, C. Jiang, and M. Zhou, " Time-soundness of time Petri nets modeling time-critical systems,” ACM Trans. Cyber-Physical Systems, 2(2), DOI: 10.1145/3185502,2018.
[34]	G. Agha and K. Palmskog, " A Survey of Statistical Model Checking, ” ACM Trans. Modeling and Computer Simulation, vol. 28, no. 1, 2018.

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(18) / Tables(1)

Get Citation

PDF

XML

Article Metrics

Article views (1851) PDF downloads(70)

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.

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

doi: 10.1109/JAS.2019.1911576

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Highlights

Export File

Citation

Format

Content