Journal of Intelligent Manufacturing

, Volume 27, Issue 1, pp 131–148 | Cite as

A self-adaptation scheme for workflow management in multi-agent systems

  • Fu-Shiung HsiehEmail author
  • Jim-Bon Lin


Business processes, operational environment, variability of resources and user needs may change from time to time. An effective workflow management software system must be able to accommodate these changes. The ability to dynamically adapt to changes is a key success factor for workflow management systems. Holonic multi-agent systems (HMS) provide a flexible and reconfigurable architecture to accommodate changes based on dynamic organization and collaboration of autonomous agents. Although HMS provides a potential architecture to accommodate changes, the dynamic organization formed in HMS poses a challenge in the development of a new software development methodology to dynamically compose the services and adapt to changes as needed. This motivates us to study and propose a methodology to design self-adaptive software systems based on the HMS architecture. In this paper, we formulate a workflow adaptation problem (WAP) and propose an interaction mechanism based on contract net protocol (CNP) to find a solution to WAP to compose the services based on HMS. The interaction mechanism relies on a service publication and discovery scheme to find a set of task agents and a set of actor agents to compose the required services in HMS. We propose a viable self-adaptation scheme to reconfigure the agents and the composed services based on cooperation of agents in HMS to accommodate the changes in workflow and capabilities of actors. We propose architecture for our design methodology and present an application scenario to illustrate our idea.


Workflow Holonic system Multi-agent system Self-adaptation Petri nets 



This paper is currently supported in part by National Science Council of Taiwan under Grant NSC102-2410-H-324-014-MY3.


  1. Ambros-Ingerson, J., & Steel, S. (1988). Integrating planning, execution and monitoring. In 7th national conference on artificial intelligence (AAAI-88). St. Paul, USA.Google Scholar
  2. ATHENA (2003). Advanced technologies for interoperability of heterogeneous enterprise networks and their applications. FP6-2002-IST1, Integrated project.Google Scholar
  3. A.O. Software. (2012). JACK intelligent agents manual.
  4. B. Telecommunications. (2002). Zeus.
  5. Balasubramanian, S., Brennan, R. W., & Norrie, D. H. (2001). An architecture for metamorphic control of holonic manufacturing systems. Computers in Industry, 46, 13–31.CrossRefGoogle Scholar
  6. Billington, J., Christensen, S., van Hee, K., Kindler, E., Kummer, O., Petrucci, L., et al. (2003). The Petri net markup language: concepts, technology and tools. Lecture notes in computer science (Vol. 2679, pp. 483–505).Google Scholar
  7. Blanc, S., Ducq, Y., & Vallespir, B. (2007). Evolution management towards interoperable supply chains using performance measurement. Computers in Industry, 58(7), 720–732.CrossRefGoogle Scholar
  8. Bratman, M. E., Israel, D. J., & Pollack, M. E. (1988). Plans and resourcebounded practical reasoning. Computational Intelligence, 4, 349–355.CrossRefGoogle Scholar
  9. Bresciani, P., Giorgini, P., Giunchiglia, F., Mylopoulos, J., & Perini, A. (2004). TROPOS: An agent oriented software development methodology. Journal of Autonomous Agents and Multi-agent Systems, 8(3), 203–236.Google Scholar
  10. C4ISR Architecture Framework. (1998). Levels of information systems interoperability (LISI).Google Scholar
  11. Chen, D., & Vernadat, F. (2002). Enterprise interoperability: A standardisation view. In K. Kosanke, et al. (Eds.), Enterprise inter-and-intra organisational integration (pp. 273–282). Dordrecht: Kluwer. ISBN:1-4020-7277-5.Google Scholar
  12. Chen, D., Dassisti, M., & Tsalgatidou, A. (2005). Interoperability knowledge corpus. Deliverable DI.1. Workpackage DI, INTEROP NoE, November 25,2005.Google Scholar
  13. Chen, D., & Vernadat, F. (2004). Standards on enterprise integration and engineering—a state of the art. International Journal of Computer Integrated Manufacturing, 17(3), 235–253.CrossRefGoogle Scholar
  14. Chen, D., Doumeingts, G., & Vernadat, F. (2008). Architectures for enterprise integration and interoperability: Past, present and future. Computers in Industry, 59, 647–659.CrossRefGoogle Scholar
  15. Chituc, C.-M., Toscano, C., & Azevedo, A. (2008). Interoperability in collaborative networks: Independent and industry-specific initiatives—the case of the footwear industry. Computers in Industry, 59, 741–757.CrossRefGoogle Scholar
  16. Chituc, C.-M., Azevedo, A., & Toscano, C. (2009). A framework proposal for seamless interoperability in a collaborative networked environment. Computers in Industry, 60(5), 317–338.CrossRefGoogle Scholar
  17. Christensen, J. (1994). Holonic manufacturing systems—initial architecture and standards directions. In Proceedings of the first European conference on holonic manufacturing systems, Hannover, Germany.Google Scholar
  18. Cicirelli, F., Furfaro, A., & Nigro, L. (2010). A service-based architecture for dynamically reconfigurable workflows. The Journal of Systems and Software, 83, 1148–1164.CrossRefGoogle Scholar
  19. Cossentino, M. (2005). From requirements to code with the PASSI methodology. In B. Henderson-Sellers & P. Giorgini (Eds.), Agent-oriented methodologies (pp. 79–106). Hershey, PA: Idea Group.Google Scholar
  20. Coulson, G., Blair, G., Grace, P., Taiani, F., Joolia, A., Lee, K., et al. (2008). A generic component model for building systems software. ACM Transactions on Computer Systems, 26(1), 1–42.Google Scholar
  21. DeLoach, S. A., & Kumar, M. (2005). Multi-agent systems engineering: An overview and case study. In B. Henderson-Sellers & P. Giorgini (Eds.), Agent-oriented methodologies (pp. 236–276). Hershey, PA: IDEA Group Publishing.Google Scholar
  22. Emorphia. (2003). FIPA-OS.
  23. Ferber, J., & Gutknecht, O. (1998). A meta-model for the analysis and design of organizations in multi-agent systems. In 3rd international conference on multi-agent systems (ICMAS’98), pp. 128–135. Paris, France.Google Scholar
  24. Fikes, R. E., & Nilsson, N. (1971). STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 5(2), 189–208.CrossRefGoogle Scholar
  25. FIPA. (2002a). FIPA ACL message structure specification.
  26. FIPA. (2002b). FIPA interaction protocols specifications.
  27. Garlan, D., Cheng, S. W., Huang, A. C., Schmerl, B., & Steenkiste, P. (2004). Rainbow: Architecture-based self-adaptation with reusable infrastructure. Computer, 37(10), 46–49.CrossRefGoogle Scholar
  28. Hsieh, F. S. (2008). Holarchy formation and optimization in holonic manufacturing systems with contract net. Automatica, 44(4), 959–970.CrossRefGoogle Scholar
  29. Hsieh, F. S. (2009). Dynamic composition of holonic processes to satisfy timing constraints with minimal costs. Engineering Applications of Artificial Intelligence, 22(7), 1117–1126.CrossRefGoogle Scholar
  30. Hsieh, F. S. (2010). Design of reconfiguration mechanism for holonic manufacturing systems based on formal models. Engineering Applications of Artificial Intelligence, 23(7), 1187–1199.CrossRefGoogle Scholar
  31. Hsieh, F. S., & Chiang, C. Y. (2011). Collaborative composition of processes in holonic manufacturing systems. Computers in Industry, 62(1), 51–64.CrossRefGoogle Scholar
  32. Hsieh, F. S., & Lin, J. B. (2012). Context-aware workflow management for virtual enterprises based on coordination of agents. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-012-0688-8.
  33. Huhns, M. N., & Singh, M. P. (2005). Service-oriented computing: Key concepts and principles. IEEE Internet Computing, 9(1), 75–81.CrossRefGoogle Scholar
  34. IDEAS (2002). Thematic network, IDEAS: Interoperability development for enterprise application and software–roadmaps, Annex 1–DoW.Google Scholar
  35. Iglesias, C. A., & Garijo, M. (2005). The agent-oriented methodology MAS-commonKADS. In B. Henderson-Sellers & P. Giorgini (Eds.), Agent-oriented methodologies (pp. 46–78). Hershey, PA: IDEA Group Publishing.CrossRefGoogle Scholar
  36. ISO 11354-1. (2011). Advanced automation technologies and their applications—requirements for establishing manufacturing enterprise process interoperability—part 1: Framework for enterprise interoperability.Google Scholar
  37. ISO 18629-1. (2004). Industrial automation systems and integration—process specification language—part 1: Overview and basic principles.Google Scholar
  38. ISO/IEC 15909-2. (2011). Systems and software engineering—high-level Petri nets—part 2: Transfer format.Google Scholar
  39. Kasten, E. P., & McKinley, P. K. (2004). Perimorph: Run-time composition and state management for adaptive systems. In 24th international conference on distributed computing systems workshops, proceedings, pp. 332–337.Google Scholar
  40. Koestler, A. (1967). The ghost in the machine. London: Hutchinson.Google Scholar
  41. Kon, F., Marques, J. R., Yamane, T., Campbell, R. H., & Mickunas, M. D. (2005). Design, implementation, and performance of an automatic configuration service for distributed component systems. Software-Practice & Experience, 35(7), 667–703.CrossRefGoogle Scholar
  42. KQML Specification Document. (1993). UMBC AgentWeb.
  43. Leitão, P., & Restivo, F. (2006). ADACOR: A holonic architecture for agile and adaptive manufacturing control. Computers in Industry, 57(2), 121–130.CrossRefGoogle Scholar
  44. Murata, T. (1989). Petri nets: Properties. analysis and applications. Proceedings of the IEEE, 77(4), 541–580.Google Scholar
  45. Naudet, Y., Latour, T., Guedria, W., & Chen, D. (2010). Towards a systemic formalisation of interoperability. Computers in Industry, 61(2), 176–185.CrossRefGoogle Scholar
  46. OASIS. (2009). Web services business process execution language version 2.0.
  47. Object Management Group, (2009). Business process modeling notation.
  48. Oreizy, P., Gorlick, M. M., Taylor, R. N., Heimbigner, D., Johnson, G., Medvidovic, N., et al. (1999). An architecture-based approach to self-adaptive software. IEEE Intelligent Systems & Their Applications, 14(3), 54–62.CrossRefGoogle Scholar
  49. Ort, E. (2005). Service-oriented architecture and web services: Concepts, technologies, and tools. Available at
  50. Padgham, L., & Winikoff, M. (2005). Prometheus: A practical agent-oriented methodology. In B. Henderson-Sellers & P. Giorgini (Eds.), Agent-oriented methodologies (pp. 107–135). Hershey, PA: IDEA Group Publishing.CrossRefGoogle Scholar
  51. Panetto, H., & Molina, A. (2008). Enterprise integration and interoperability in manufacturing systems: Trends and issues. Computers in Industry, 59(7), 641–646.CrossRefGoogle Scholar
  52. Rinderle, S., Reichert, M., & Dadam, P. (2004). Correctness criteria for dynamic changes in workflow systems: A survey. Data and Knowledge Engineering, 50(1), 9–34.CrossRefGoogle Scholar
  53. Smith, R. G. (1980). The contract net protocol: High-level communication and control in a istributed problem solver. IEEE Transactions on Computers, 29(12), 1104–1113.CrossRefGoogle Scholar
  54. SOAP Specification. (2007).
  55. Tran, Q.-N. N., & Low, G. (2008). MOBMAS: A methodology for ontology-based multi-agent systems development. Information and Software Technology, 50, 697–722.CrossRefGoogle Scholar
  56. UDDI Specification. (2007).
  57. Web Services Description Language (WSDL) Specification. (2001).
  58. van der Aalst, W. M. P. (1998). The application of Petri nets to workflow management. Journal of Circuits, Systems, and Computers, 8(1), 21–66.CrossRefGoogle Scholar
  59. van der Aalst, W. M. P., & Basten, T. (2002). Inheritance of workflows: An approach to tackling problems related to change. Theoretical Computer Science, 270(1), 125–203.CrossRefGoogle Scholar
  60. van der Aalst, W. M. P., & Jablonski, S. (2000). Dealing with workflow change: Identification of issues and solutions. International Journal of Computer Systems Science and Engineering, 15(5), 267–276.Google Scholar
  61. Weber, M., & Kindler, E. (2002). The Petri net markup language.
  62. Weichhart, G., Feiner, T., & Stary, C. (2010). Implementing organizational interoperability—the SUddEN approach. Computers in Industry, 61(2), 152–160.Google Scholar
  63. Wood, S. (1993). Planning and decision making in dynamic domains. Chichester: Ellis Horwood.Google Scholar
  64. Wooldridge, M. (1999). Intelligent agents. In G. Weiss (Ed.), Multiagent systems: A modern approach to distributed artificial intelligence (pp. 27–77). London: The MIT Press.Google Scholar
  65. Wooldridge, M., Jennings, N. R., & Zambonelli, F. (2005). Multi-agent systems as computational organizations: The Gaia methodology. In B. Henderson-Sellers & P. Giorgini (Eds.), Agent-oriented methodologies (pp. 136–171). Hershey, PA: IDEA Group Publishing.Google Scholar
  66. Workflow Management Coalition. (1998).
  67. Workflow Management Coalition. (1999). The workflow management coalition specifications: Terminology and glossary.
  68. Wyns, J. (1999). Reference architecture for holonic manufacturing. Ph.D. dissertation, PMA Division, Katholieke Universiteit, Leuven, Belgium.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringChaoyang University of TechnologyTaichungTaiwan

Personalised recommendations