Workflow Management Systems + Swarm Intelligence = Dynamic Task Assignment for Emergency Management Applications

  • Hajo A. Reijers
  • Monique H. Jansen-Vullers
  • Michael zur Muehlen
  • Winfried Appl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4714)


The assignment of tasks to human performers is a critical component in people-centric business process management systems. Workflow management systems typically assign work items using strategies that only consider qualified resources. There are, however, situations, where this approach falls short. For instance, in emergency response situations, tasks need to be carried out by resources that are available immediately, even if they do not match all skill requirements. This paper compares the performance of a set of six task assignment mechanisms for workflow applications using a scenario from the emergency management domain. In particular, we develop and simulate assignment strategies inspired by stimulus/response models derived from swarm intelligence, and benchmark these strategies against conventional task assignment strategies. Our findings show that swarm intelligence-based approaches outperform the traditional assignment of tasks in ad-hoc organizations, and that workflow-based emergency management systems could benefit significantly from these novel task assignment strategies.


Business Process Management Workflow Task Assignment Swarm Intelligence 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    WfMC Terminology and Glossary, 3rd edn. Workflow Management Coalition, Winchester (ID) (1999)Google Scholar
  2. 2.
    Moore, C.: Common Mistakes in Workflow Implementations. Giga Information Group, Cambridge, MA (2002)Google Scholar
  3. 3.
    Kumar, A., van der Aalst, W.M.P., Verbeek, H.M.W.: Dynamic Work Distributio. In: Workflow Management Systems: How to Balance Quality and Performance, Journal of Management Information Systems 18(3) (2002)Google Scholar
  4. 4.
    Zur Mühlen, M.: Organizational Management in Workflow Applications – Issues and Perspectives. Information Technology and Management 5, 271–291 (2004)CrossRefGoogle Scholar
  5. 5.
    Swersey, A.J.: The Deployment of Police, Fire and Emergency Medical Units. In: Pollock, S.M., et al. (eds.) Handbooks in OR&MS, vol. 6, Elsevier Science, Amsterdam (1994)Google Scholar
  6. 6.
    Beni, G., Wang, J.: Swarm intelligence in cellular robotic systems. In: Proc. NATO Advanced Workshop on Robotics and Biological Systems, Il Ciocco, Tuscany, Italy (1989)Google Scholar
  7. 7.
    Meyer, C., Bonabeau, E.: Swarm Intelligence: A Whole New Way to Think About Business, Harvard Business Review, May 2001, pp. 106–117 (2001)Google Scholar
  8. 8.
    Ghizzioli, R., Nouyan, S., Birattari, M., Dorigo, M.: An Ant-Based Algorithm for the Dynamic Task assignment Problem, TR/IRIDIA (2004)Google Scholar
  9. 9.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems, Santa Fe Institute Studies in the Sciences of Complexity. Oxford University Press, Oxford (1999)Google Scholar
  10. 10.
    Bonabeau, E., Sobkowski, A., Theraulaz, G., Deneubourg, J.L.: Adaptive task allocation inspired by a model of division of labor in social insects; Biocomputation and Emergent Computing. World Scientific, Singapore (1997)Google Scholar
  11. 11.
    Price, R., Tino, P.: Evaluation Of Adaptive Nature Inspired Task Allocation Against Alternate Decentralized Multi-agent Strategies. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) Parallel Problem Solving from Nature - PPSN VIII. LNCS, vol. 3242, pp. 982–990. Springer, Heidelberg (2004)Google Scholar
  12. 12.
    Robinson, G.: Regulation of the division of labor in insect societies. Annual review, Entomol. 37, 637–665 (1992)CrossRefGoogle Scholar
  13. 13.
    Wilson, E.: The sociogenisis of insect colonies. Science 228, 1489–1495 (1985)CrossRefGoogle Scholar
  14. 14.
    Theraulaz, G., Bonabeau, E., Deneubourg, J.: Response threshold reinforcement division of labour in insects societies. In: Proceedings Royal Societies, London (1998)Google Scholar
  15. 15.
    Campos, M., Bonabeau, E., Theraulaz, G., Deneubourg, J.: Dynamic Scheduling and Division of Labor in Social Insects. Adaptive behaviour 8, 83–92 (2001)CrossRefGoogle Scholar
  16. 16.
    Austin Fire Department: Austin Fire Department, Austin City Connections (2006),
  17. 17.
    Centraal Bureau voor de Statistiek, Brandweerstatistiek 2004; Voorburg/Heerlen (2004)Google Scholar
  18. 18.
    van der Aalst, W.M.P., van Hee, K.: Workflow Management: Models, Methods and Systems. The MIT Press, Cambridge, Massachusetts (2002)Google Scholar
  19. 19.
    Gerkey, B., Mataric, M.: Multi-Robot Task assignment: Analyzing the Complexity and Optimality of Key Architectures. In: Proceedings of the 2003 IEEE International Conference on Robotics and Automation, Taipei, Taiwan (2003)Google Scholar
  20. 20.
    Gottlieb, J., Puchta, A., Solnon, C.: A Study of Greedy, Local Search, and Ant Colony Optimization Approaches for Car Sequencing Problems. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoIASP 2003, EvoWorkshops 2003, EvoSTIM 2003, EvoROB/EvoRobot 2003, EvoCOP 2003, EvoBIO 2003, and EvoMUSART 2003. LNCS, vol. 2611, Springer, Heidelberg (2003)Google Scholar
  21. 21.
    Rushton, G.: Applications of Location Models, Annual of Operations Research 18 (1989)Google Scholar
  22. 22.
    Jensen, K.: Coloured Petri Nets, Basic Concepts, Analysis Methods and Practical Use, 2nd edn., vol. 1. Springer, Heidelberg (1997)zbMATHGoogle Scholar
  23. 23.
    Mehta, A.: Smart Modeling – Basic Methodology and Advanced Tools. In: Proceedings of the 2000 Winter Simulation Conference (2000)Google Scholar
  24. 24.
    Law, A.M., Kelton, W.D.: Simulation Modeling and Analysis, 3rd edn. MCgraw Hill International Series (2000)Google Scholar
  25. 25.
    Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  26. 26.
    Cicirello, V., Smith, S.: Wasp-like Agents for Distributed Factory Coordination. Autonomous Agents and Multi-Agents Systems 8, 237–266 (2004)CrossRefGoogle Scholar
  27. 27.
    Kittithreerapronchai, O., Anderson, C.: Do ants paint trucks better than chickens? Markets versus response thresholds for distributed dynamic scheduling. In: Proceedings of the 2003 IEEE Congress on Evolutionary Computation, IEEE Press, Los Alamitos (2003)Google Scholar
  28. 28.
    Hays, W.: Statistics, 5th edn., USA, Orlando Florida (1994)Google Scholar
  29. 29.
    Cicirello, V., Smith, S.: Wasp-like Agents for Distributed Factory Coordination. Autonomous Agents and Multi-Agents Systems 8, 237–266 (2004)CrossRefGoogle Scholar
  30. 30.
    Bussler, C.: Analysis of the Organization Modeling Capability of Workflow-Management-Systems. In: Amoroso, D.L. (ed.) Conference of the Pacific Research Institute for Information Systems and Management (PRIISM 1996), Maui, HI, USA (1996)Google Scholar
  31. 31.
    Rupietta, W.: Organizational Models for Cooperative Office Applications. In: Karagiannis, D. (ed.) DEXA 1994. LNCS, vol. 856, Springer, Heidelberg (1994)Google Scholar
  32. 32.
    van der Aalst, W.M.P., Kumar, A., Verbeek, H.M.W.: Organizational Modeling in UML and XML in the context of Workflow Systems. In: Matsui, M., Zuccherato, R.J. (eds.) SAC 2003. LNCS, vol. 3006, Springer, Heidelberg (2004)Google Scholar
  33. 33.
    Momotko, M., Subieta, K.: Dynamic Changes in Workflow Participant Assignment. In: Manolopoulos, Y., Návrat, P. (eds.) ADBIS 2002. LNCS, vol. 2435, Springer, Heidelberg (2002)Google Scholar
  34. 34.
    Shen, M., Tzen, G.-H., Lio, D.-R.: Multi-Criteria Task Assignment in Workflow Management Systems. In: Sprague, R.J. (ed.) 36th HICSS, IEEE, Waikoloa, HI (2003)Google Scholar
  35. 35.
    Huang, Y.-N., Shan, M.-C.: Policy-Based Resource Management. In: Jarke, M., Oberweis, A. (eds.) CAiSE 1999. LNCS, vol. 1626, Springer, Heidelberg (1999)Google Scholar
  36. 36.
    Bussler, C., Jablonski, S.: Policy Resolution for Workflow Management. In: Sprague, R.J. (ed.) HICSS 1995. 28th Hawaii International Conference on Systems Sciences, IEEE, Hawaii (1995)Google Scholar
  37. 37.
    Gomoluch, J., Schroeder, M.: Market-Based Resource Allocation for Grid Computing: A Model and Simulation. In: Proc. 1st. Int’l. Workshop on Middleware for Grid Computing (MGC) (2003)Google Scholar
  38. 38.
    Krieger, M.J.B., Billeter, J.-B., et al.: Ant-like task allocation and recruitment in cooperative robots. Nature 406(31), 992–995 (2000)Google Scholar
  39. 39.
    Shehory, O., Kraus, S.: Methods for task allocation via agent coalition formation. Artificial Intelligence Journal 101(1-2), 165–200 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  40. 40.
    Tan, J.C., Harker, P.T.: Designing Workflow Coordination: Centralized Versus Market-Based Mechanisms. Information Systems Research 10, 328–342 (1999)CrossRefGoogle Scholar
  41. 41.
    Alt, R., Klein, S., Kuhn, C.: Service Task Allocation as an Internal Market. In: Baets, W.R.J. (ed.) ECIS 1994. Second European Conference on Information Systems, pp. 424–432. Nijenrode University Press, Netherlands (1994)Google Scholar
  42. 42.
    Wellman, M.P., Walsh, W.E., Wurman, P.R., MacKie-Mason, J.K.: Auction protocols for decentralized scheduling. Games and Economic Behavior 35, 271–303 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  43. 43.
    Baggio, G., Wainer, J., Ellis, C.: Applying scheduling techniques to minimize the number of late jobs in workflow systems. In: Handschuh, H., Hasan, M.A. (eds.) SAC 2004. LNCS, vol. 3357, pp. 1396–1403. Springer, Heidelberg (2004)Google Scholar
  44. 44.
    Bonin, G.: Final Report, Assessment of Fire and EMS Services Branchburg Township, New Jersey; Tridata, A Division of Sustem Planning Corporation (2005)Google Scholar
  45. 45.
    Mehrotra, S., Butts, C., Kalashnikov, N.: Project Rescue: Challenges in Responding to the Unexpected. SPIE 5304, 179–192 (2004)CrossRefGoogle Scholar
  46. 46.
    Amer, A., Brustoloni, J., Chrysanthis, P.K., Hauskrecht, M., Labrinidis, A., Melhem, R., Mosse, D., Pruhs, K., Comfort, L.: Secure-CITI: A Secure Critical Information Technology Infrastructure for Disaster Management, Hazard Reduction and Response in Metropolitan Regions (2003)Google Scholar
  47. 47.
    Freßmann, A.: Adaptive Workflow Support for Search Processes within Fire Service Organisations, University of Trier (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Hajo A. Reijers
    • 1
  • Monique H. Jansen-Vullers
    • 1
  • Michael zur Muehlen
    • 2
  • Winfried Appl
    • 2
  1. 1.Eindhoven University of Technology, Department of Management Technology, Den Dolech 2, 5600 MB, EindhovenThe Netherlands
  2. 2.Stevens Institute of Technology, Howe School of Technology Management, Castle Point on Hudson, Hoboken, NJ 07030USA

Personalised recommendations