Resource Allocation with Dependencies in Business Process Management Systems

  • Giray HavurEmail author
  • Cristina Cabanillas
  • Jan Mendling
  • Axel Polleres
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 260)


Business Process Management Systems (BPMS) facilitate the execution of business processes by coordinating all involved resources. Traditional BPMS assume that these resources are independent from one another, which justifies a greedy allocation strategy of offering each work item as soon as it becomes available. In this paper, we develop a formal technique to derive an optimal schedule for work items that have dependencies and resource conflicts. We build our work on Answer Set Programming (ASP), which is supported by a wide range of efficient solvers. We apply our technique in an industry scenario and evaluate its effectiveness. In this way, we contribute an explicit notion of resource dependencies within BPMS research and a technique to derive optimal schedules.


Answer Set Programming Optimality Resource allocation Resource requirements Work scheduling 


  1. 1.
    Rummler, G.A., Ramias, A.J.: A framework for defining and designing the structure of work. In: vom Brocke, J., Rosemann, M. (eds.) Handbook on Business Process Management 1, pp. 81–104. Springer, Heidelberg (2015)Google Scholar
  2. 2.
    Reijers, H.A., Vanderfeesten, I.T.P., van der Aalst, W.M.P.: The effectiveness of workflow management systems: a longitudinal study. Int. J. Inf. Manage. 36(1), 126–141 (2016)CrossRefGoogle Scholar
  3. 3.
    Rosemann, M., vom Brocke, J.: The six core elements of business process management. In: vom Brocke, J., Rosemann, M. (eds.) Handbook on Business Process Management 1, pp. 105–122. Springer, Heidelberg (2015)Google Scholar
  4. 4.
    Mans, R., Russell, N.C., Aalst, W.M.P., Moleman, A.J., Bakker, P.J.M.: Schedule-aware workflow management systems. Trans. Petri Nets Other Models Concurrency 4, 121–143 (2010)Google Scholar
  5. 5.
    Havur, G., Cabanillas, C., Mendling, J., Polleres, A.: Automated resource allocation in business processes with answer set programming. In: Reichert, M., Reijers, H. (eds.) BPM Workshops 2015. LNBIP, vol. 256, pp. 191–203. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-42887-1_16 CrossRefGoogle Scholar
  6. 6.
    Popova-Zeugmann, L.: Time Petri Nets, pp. 139–140, Springer, Heidelberg (2013)Google Scholar
  7. 7.
    Johnson, D.S., Garey, M.R.: Computers and Intractability: A Guide to the Theory of NP-Completeness. WH Free. Co., San Fr. (1979)zbMATHGoogle Scholar
  8. 8.
    Buccafurri, F., Leone, N., Rullo, P.: Enhancing disjunctive datalog by constraints. IEEE Trans. Knowl. Data Eng. 12(5), 845–860 (2000)CrossRefGoogle Scholar
  9. 9.
    Ouyang, C., Wynn, M.T., Fidge, C., ter Hofstede, A.H., Kuhr, J.-C.: Modelling complex resource requirements in Business Process Management Systems. In: ACIS (2010)Google Scholar
  10. 10.
    Brickley, D., Guha, R.: RDF Schema 1.1. W3C Recommendation, February 2014.
  11. 11.
    Beckett, D., Berners-Lee, T., Prud’hommeaux, E., Carothers, G.: Turtle - Terse RDF Triple Language. W3C Candidate Recommendation, February 2014.
  12. 12.
    OMG, BPMN 2.0, recommendation, OMG (2011)Google Scholar
  13. 13.
    Cabanillas, C., Resinas, M., Río-Ortega, A., Ruiz-Cortés, A.: Specification and automated design-time analysis of the business process human resource perspective. Inf. Syst. 52, 55–82 (2015)CrossRefGoogle Scholar
  14. 14.
    Aalst, W.M.P., Hofstede, A.H.M.: YAWL: yet another workflow language. Inf. Syst. 30(4), 245–275 (2005)CrossRefGoogle Scholar
  15. 15.
    Stroppi, L.J.R., Chiotti, O., Villarreal, P.D.: A BPMN 2.0 extension to define the resource perspective of business process models. In: CIbS 2011 (2011)Google Scholar
  16. 16.
    Cabanillas, C., Resinas, M., Mendling, J., Cortés, A.R.: Automated team selection and compliance checking in business processes. In: ICSSP, pp. 42–51 (2015)Google Scholar
  17. 17.
    Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Answer Set Solving in Practice. Morgan & Claypool Publishers, San Rafael (2012)zbMATHGoogle Scholar
  18. 18.
    Van Nieuwenborgh, D., De Cock, M., Hadavandi, E.: Fuzzy answer set programming. In: Fisher, M., van der Hoek, W., Konev, B., Lisitsa, A. (eds.) JELIA 2006. LNCS (LNAI), vol. 4160, pp. 359–372. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  19. 19.
    van der Aalst, W.: Petri net based scheduling. Operations-Research-Spektrum 18(4), 219–229 (1996)Google Scholar
  20. 20.
    Roose, R.: Automated Resource Optimization in Business Processes. MSc. ThesisGoogle Scholar
  21. 21.
    Eiter, T., Faber, W., Leone, N., Pfeifer, G., Polleres, A.: Answer set planning under action costs. J. Artif. Intell. Res. (JAIR) 19, 25–71 (2003)MathSciNetzbMATHGoogle Scholar
  22. 22.
    Brewka, G., Eiter, T., Truszczyński, M.: Answer set programming at a glance. Commun. ACM 54(12), 92–103 (2011)CrossRefGoogle Scholar
  23. 23.
    Calimeri, F., Gebser, M., Maratea, M., Ricca, F.: Design and results of the fifthanswer set programming competition. Artif. Intell. 231, 151–181 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Eiter, T., Ianni, G., Krennwallner, T., Polleres, A.: Rules and ontologies for the semantic web. In: Baroglio, C., Bonatti, P.A., Małuszyński, J., Marchiori, M., Polleres, A., Schaffert, S. (eds.) Reasoning Web 2008. LNCS, vol. 5224, pp. 1–53. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  25. 25.
    Castro, P.M., Marques, I.: Operating room scheduling with generalized disjunctive programming. Comput. Oper. Res. 64, 262–273 (2015)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Silva, T.A., Souza, M.C., Saldanha, R.R., Burke, E.K.: Surgical scheduling with simultaneous employment of specialised human resources. Eur. J. Oper. Res. 245(3), 719–730 (2015)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Riise, A., Mannino, C., Burke, E.K.: Modelling and solving generalised operational surgery scheduling problems. Comput. Oper. Res. 66, 1–11 (2016)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Siu, M.-F.F., Lu, M., AbouRizk, S.: Methodology for crew-job allocation optimization in project and workface scheduling. In: ASCE, pp. 652–659 (2015)Google Scholar
  29. 29.
    Menesi, W., Abdel-Monem, M., Hegazy, T., Abuwarda, Z.: Multi-objective schedule optimization using constraint programming. In: ICSC15 (2015)Google Scholar
  30. 30.
    Sprecher, A., Drexl, A.: Multi-mode resource-constrained project scheduling by a simple, general and powerful sequencing algorithm1. Eur. J. Oper. Res. 107(2), 431–450 (1998)CrossRefzbMATHGoogle Scholar
  31. 31.
    Senkul, P., Toroslu, I.H.: An architecture for workflow scheduling under resource allocation constraints. Inf. Syst. 30, 399–422 (2005)CrossRefGoogle Scholar
  32. 32.
    Arias, M., Rojas, E., Munoz-Gama, J., Sepúlveda, M.: A framework for recommending resource allocation based on process mining. In: BpPM Workshops (DeMiMoP) (in press) (2015)Google Scholar
  33. 33.
    Lombardi, M., Milano, M.: Optimal methods for resource allocation and scheduling: a cross-disciplinary survey. Constraints 17, 51–85 (2012)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Rieck, J., Zimmermann, J.: Exact methods for resource leveling problems. In: Schwindt, C., Zimmermann, J. (eds.) Handbook on Project Management and Scheduling, vol. 1. Springer, Switzerland (2015)Google Scholar
  35. 35.
    Lohmann, N., Verbeek, E., Dijkman, R.: Petri Net transformations for business processes - a survey. Trans. Petri Nets Other Models Concurrency II (2), 46–63 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Giray Havur
    • 1
    Email author
  • Cristina Cabanillas
    • 1
  • Jan Mendling
    • 1
  • Axel Polleres
    • 1
  1. 1.Vienna University of Economics and BusinessViennaAustria

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