Evaluating the Performance of a Batch Activity in Process Models

  • Luise Pufahl
  • Ekaterina BazhenovaEmail author
  • Mathias Weske
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 202)


The goal of many organizations of today is optimization of business process management. A factor for optimization of business processes is reduction of costs associated with mass production and customer service. Recently, an approach to incorporate batch activities in process models was proposed to improve the process performance by synchronizing a group of process instances. However, the issue of optimal utilization of batch activities and estimation of associated costs remained still open. In this paper, we present an approach to evaluate batch activity performance, based on techniques from queuing theory. Thus, cost functions are introduced in order to (1) compare usual (i.e., non-batch) and batch activity execution and (2) find the optimal configuration of a batch activity. The approach is applied to a real-world use case from the healthcare domain.


Process analysis Batch activity Cost function Queuing theory 


  1. 1.
    Davis, M.M.: How long should a customer wait for service? Decis. Sci. 22(2), 421–434 (1991)CrossRefGoogle Scholar
  2. 2.
    Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Ferguson, Mark E., Jayaraman, Vaidy, Souza, Gilvan C.: Note: an application of the EOQ model with nonlinear holding cost to inventory management of perishables. Eur. J. Oper. Res. 180(1), 485–490 (2007)CrossRefzbMATHGoogle Scholar
  4. 4.
    Kendall, D.G.: Stochastic processes occurring in the theory of queues and their analysis by the method of the imbedded Markov chain. Ann. Math. Stat. 24(3), 338–354 (1953)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Kleinrock, L.: Queueing Systems. Theory, vol. I. Wiley Interscience, New York (1975)zbMATHGoogle Scholar
  6. 6.
    Liu, J., Hu, J.: Dynamic batch processing in workflows: model and implementation. Future Gener. Comput. Syst. 23(3), 338–347 (2007)CrossRefGoogle Scholar
  7. 7.
    Medhi, J.: Stochastic Models in Queueing Theory. Academic Press Professional Inc., San Diego (1991)zbMATHGoogle Scholar
  8. 8.
    Neuts, M.: A general class of bulk queues with poisson input. Ann. Math. Stat. 38(3), 759–770 (1967)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Polack, B., Schved, J.-F., Boneu, B.: Preanalytical recommendations of the d’Etude sur l’Hémostase et la Thrombose (GEHT) for venous blood testing in hemostasis laboratories. Pathophysi. Haemost. Thromb. 31(1), 61–68 (2001)CrossRefGoogle Scholar
  10. 10.
    Pufahl, L., Meyer, A., Weske, M.: Batch regions: process instance synchronization based on data. In: Enterprise Distributed Object Computing (EDOC). IEEE (2014, accepted for publication)Google Scholar
  11. 11.
    Pufahl, Luise, Weske, Mathias: Batch activities in process modeling and execution. In: Basu, Samik, Pautasso, Cesare, Zhang, Liang, Fu, Xiang (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 283–297. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  12. 12.
    Rozinat, Anne, Mans, R.S., Song, M., van der Aalst, W.M.P.: Discovering simulation models. Inf. Syst. 34(3), 305–327 (2009)CrossRefGoogle Scholar
  13. 13.
    Sadiq, S., Orlowska, M., Sadiq, W., Schulz, K.: When workflows will not deliver: the case of contradicting work practice. In: BIS, Witold Abramowicz, vol. 1, pp. 69–84 (2005)Google Scholar
  14. 14.
    Sazvar, Zeinab, Baboli, Armand, Jokar, Mohammad Reza Akbari: A replenishment policy for perishable products with non-linear holding cost under stochastic supply lead time. Int. J. Adv. Manufact. Technol. 64(5–8), 1087–1098 (2013)CrossRefGoogle Scholar
  15. 15.
    van der Aalst, W., Barthelmess, P., Ellis, C., Wainer, J.: Proclets: a framework for lightweight interacting workflow processes. IJCIS 10(4), 443–481 (2001)Google Scholar
  16. 16.
    Weiss, H., Pliska, S.: Optimal control of some Markov processes with applications to batch queueing and continuous review inventory systems. The Center for Mathematical Studies in Economics and Management Science, Discussion Paper 214 (1976)Google Scholar
  17. 17.
    Weiss, Howard J.: The computation of optimal control limits for a queue with batch services. Manage. Sci. 25(4), 320–328 (1979)CrossRefzbMATHGoogle Scholar
  18. 18.
    Weske, M.: Business Process Management: Concepts, Languages, Architectures, 2nd edn. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Luise Pufahl
    • 1
  • Ekaterina Bazhenova
    • 1
    Email author
  • Mathias Weske
    • 1
  1. 1.Hasso Plattner Institute at the University of PotsdamPotsdamGermany

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