A Case Study in Workflow Scheduling Driven by Log Data

  • Mirela BotezatuEmail author
  • Hagen Völzer
  • Remco Dijkman
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 202)


This paper shows through a case study the potential for optimizing resource allocation in business process execution. While most resource allocation mechanisms focus on assigning resources to the tasks that they are authorized to perform, we assign resources to the tasks that they can provably perform most efficiently, by mining the execution logs. This gives rise to the minimization of the cost of the process execution. We present various cost measures and how hybrid algorithms can balance their conflicting goals. Our case study indicates significant potential for further research into optimal resource allocation mechanisms.


Mathematical optimization of business processes Static and dynamic optimization Performance measurement of business processes Resource allocation in business processes 


  1. 1.
    Code for the analysis. - pwd: P\({\$}\)P3119-515-FF\({\$}\) \({\$}\)sdcB8-1
  2. 2.
    Baggio, G., Wainer, J., Ellis, C.: Applying scheduling techniques to minimize the number of late jobs in workflow systems. In: Proceedings of the 2004 ACM Symposium on Applied Computing, SAC 2004, pp. 1396–1403. ACM, New York (2004)Google Scholar
  3. 3.
    Baykasoğlu, A., Göçken, M., Özbakir, L.: Genetic programming based data mining approach to dispatching rule selection in a simulated job shop. Simulation 86(12), 715–728 (2010)CrossRefGoogle Scholar
  4. 4.
    Buzacott, J.A., Yao, D.D.: On queueing network models of flexible manufacturing systems. Queueing Syst. 1(1), 5–27 (1986)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Combi, C., Pozzi, G.: Task scheduling for a temporalworkflow management system. In: 2006 Thirteenth International Symposium on Temporal Representation and Reasoning, TIME 2006, pp. 61–68, June 2006Google Scholar
  6. 6.
    Graham, R.L.: Bounds for certain multi-processing anomalies. Bell Syst. Tech. J. 45(9), 1563–1581 (1966)CrossRefGoogle Scholar
  7. 7.
    Kumar, A., Dijkman, R., Song, M.: Optimal resource assignment in workflows for maximizing cooperation. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 235–250. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  8. 8.
    Kumar, A., Van Der Aalst, W.M.P., Verbeek, E.M.W.: Dynamic work distribution in workflow management systems: How to balance quality and performance. J. Manage. Inf. Syst. 18(3), 157–193 (2002)Google Scholar
  9. 9.
    Liu, Y., Wang, J., Yang, Y., Sun, J.: A semi-automatic approach for workflow staff assignment. Comput. Indus. 59(5), 463–476 (2008)CrossRefGoogle Scholar
  10. 10.
    Ly, L.T., Rinderle, S., Dadam, P., Reichert, M.: Mining staff assignment rules from event-based data. In: Bussler, C.J., Haller, A. (eds.) BPM 2005. LNCS, vol. 3812, pp. 177–190. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  11. 11.
    Priore, P., De La Fuente, D., Gomez, A., Puente, J.: A review of machine learning in dynamic scheduling of flexible manufacturing systems. AI EDAM 15(3), 251–263 (2001)zbMATHGoogle Scholar
  12. 12.
    Reijers, H.A., Jansen-Vullers, M.H., zur Muehlen, M., Appl, W.: Workflow management systems + swarm intelligence = dynamic task assignment for emergency management applications. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 125–140. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  13. 13.
    Rinderle-Ma, S., van der Aalst, W.M.P.: Life-cycle support for staff assignment rules in process-aware information systems. Technical report, TU Eindhoven (2007)Google Scholar
  14. 14.
    Russell, N., van der Aalst, W.M.P., ter Hofstede, A.H.M., Edmond, D.: Workflow resource patterns: identification, representation and tool support. In: Pastor, Ó., Falcão e Cunha, J. (eds.) CAiSE 2005. LNCS, vol. 3520, pp. 216–232. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  15. 15.
    Jin, H.S., Myoung, H.K.: Improving the performance of time-constrained workflow processing. J. Syst. Softw. 58(3), 211–219 (2001)CrossRefGoogle Scholar
  16. 16.
    Baskar, N., Premalatha, S.: Implementation of supervised statistical data mining algorithm for single machine scheduling. J. Adv. Manage. Res. 9(2), 170–177 (2012)CrossRefGoogle Scholar
  17. 17.
    Shahzad, A., Mebarki, N.: Data mining based job dispatching using hybrid simulation-optimization approach for shop scheduling problem. Eng. Appl. Artif. Intell. 25(6), 1173–1181 (2012)CrossRefGoogle Scholar
  18. 18.
    van Dongen, B.F.: Event log for the bpi challenge (2012).
  19. 19.
    Xu, J., Liu, C., Zhao, X., Yongchareon, S.: Business process scheduling with resource availability constraints. In: Meersman, R., Dillon, T.S., Herrero, P. (eds.) OTM 2010. LNCS, vol. 6426, pp. 419–427. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  20. 20.
    Xu, Z., Song, B.: A machine learning application for human resource data mining problem. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 847–856. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  21. 21.
    Yang, H., Wang, C., Liu, Y., Wang, J.: An optimal approach for workflow staff assignment based on hidden markov models. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM-WS 2008. LNCS, vol. 5333, pp. 24–26. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  22. 22.
    Muehlen, Z.: M.: Organizational management in workflow applications - issues and perspectives. Inf. Technol. Manage. 5(3–4), 271–291 (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.IBM Research – ZurichZurichSwitzerland
  2. 2.Eindhoven University of TechnologyEindhovenThe Netherlands

Personalised recommendations