Data-Driven Performance Analysis of Scheduled Processes
The performance of scheduled business processes is of central importance for services and manufacturing systems. However, current techniques for performance analysis do not take both queueing semantics and the process perspective into account. In this work, we address this gap by developing a novel method for utilizing rich process logs to analyze performance of scheduled processes. The proposed method combines simulation, queueing analytics, and statistical methods. At the heart of our approach is the discovery of an individual-case model from data, based on an extension of the Colored Petri Nets formalism. The resulting model can be simulated to answer performance queries, yet it is computational inefficient. To reduce the computational cost, the discovered model is projected into Queueing Networks, a formalism that enables efficient performance analytics. The projection is facilitated by a sequence of folding operations that alter the structure and dynamics of the Petri Net model. We evaluate the approach with a real-world dataset from Dana-Farber Cancer Institute, a large outpatient cancer hospital in the United States.
KeywordsSchedule Process Parallel Task Queueing Network Queueing Station Schedule Transition
Unable to display preview. Download preview PDF.
- 2.van der Aalst, W.M.P.: Process mining: Discovery, Conformance and Enhancement of Business Processes. Springer (2011)Google Scholar
- 6.Bause, F.: Queueing Petri nets-a formalism for the combined qualitative and quantitative analysis of systems. In: PNPM 1993, pp. 14–23. IEEE (1993)Google Scholar
- 7.Bause, F., Kritzinger, P.S.: Stochastic Petri Nets. Springer (1996)Google Scholar
- 8.Bolch, G., Greiner, S., de Meer, H., Trivedi, K.S.: Queueing networks and Markov chains: modeling and performance evaluation with computer science applications. John Wiley & Sons (2006)Google Scholar
- 9.Boxma, O., Koole, G., Liu, Z.: Queueing-theoretic solution methods for models of parallel and distributed systems. Statistics, and System Theory, Centrum voor Wiskunde en Informatica, Department of Operations Research (1994)Google Scholar
- 13.Jensen, K.: Coloured Petri nets: basic concepts, analysis methods and practical use, vol. 1. Springer (1997)Google Scholar
- 15.Pinedo, M.L.: Planning and Scheduling in Manufacturing and Services. Springer (2005)Google Scholar
- 16.Pommereau, F.: Quickly prototyping petri nets tools with SNAKES. In: Proceedings of PNTAP 2008, pp. 1–10. ACM (2008)Google Scholar
- 20.Senderovich, A., Weidlich, M., Gal, A., Mandelbaum, A.: Queue mining for delay prediction in multi-class service processes. Tech. rep. (2014)Google Scholar
- 21.Senderovich, A., Weidlich, M., Gal, A., Mandelbaum, A.: Queue mining – predicting delays in service processes. In: Jarke, M., Mylopoulos, J., Quix, C., Rolland, C., Manolopoulos, Y., Mouratidis, H., Horkoff, J. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 42–57. Springer, Heidelberg (2014) Google Scholar
- 22.Senderovich, A., Weidlich, M., Gal, A., Mandelbaum, A., Kadish, S., Bunnell, C.A.: Discovery and validation of queueing networks in scheduled processes. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.) CAiSE 2015. LNCS, vol. 9097, pp. 417–433. Springer, Heidelberg (2015) CrossRefGoogle Scholar
- 24.Vernon, M., Zahorjan, J., Lazowska, E.D.: A comparison of performance Petri nets and queueing network models. University of Wisconsin-Madison, Computer Sciences Department (1986)Google Scholar