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Applying process mining approach to support the verification of a multi-agent system

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Abstract

Using agent development tools to construct an agent-based system is a well applied approach. However, the development tools usually do not have the function to check the feasibility about the workflow of the agent system during it implementation stage. Therefore, to develop an evaluation approach to analyze the feasibility of a developing agent system such that the improper workflow of an agent system can be found in the early design stage is a necessary task to reduce the risk of implementation.

In this research, a Petri Net (PN) based three-stage evaluation approach was developed.

In the conceptual stage, the pitfall of the current agent system developing process was examined and an improvement analysis process was specified. Then, in the system design stage, an evaluation approach which extracted the process log file from a developing agent system into a PN model in terms of a process mining approach-α algorithm was proposed. This model was simulated in a PN simulation package. The agent system performance was evaluated in terms of analyzing the deadlock phenomena of the PN model. Finally, in the implementation stage, the proposed concept was implemented by using an agent developing tool JADE and a PN simulation tool CPN. An agent-based robotic assembly system was used to examine the possible deadlock of the agent system.

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References

  1. van der Aalst, W.M.P., Weijters, A.J.M.M. & Maruster, L. (2004). Workflow mining: discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, 16(9): 1128–1142

    Article  Google Scholar 

  2. van der Aalst, W.M.P., Rubin, V., van Dongen, B.F., Kindler, E. & Gunther, C.W. (2006). Process mining: a two-step approach using transition systems and regions. BPM Center Report BPM-06-30. Available via DIALOG. http://wwwis.win.tue.nl/~wvdaalst/BPMcenter/reports.htm

  3. Agrawal, R., Gunopulos, D. & Leymann, F. (1998). Mining process from workflow logs. In: Sixth International Conference on Extending Database Technology, 469–483

  4. Cardid, M., Cigolini, R. & Marco, D.D. (2005). Improving supply chain collaboration by linking intelligent agent to CPFR. International Journal of Production Research, 43(20): 4191–4218

    Article  Google Scholar 

  5. Chiu, M. & Lin, G. (2004). Collaborative supply chain planning using the artificial neural network approach. Journal of Manufacturing Technology Management, 15(8): 787–796

    Article  Google Scholar 

  6. Damm, W., Hungar, H. & Olderog, E.R. (2006). Verification of cooperating traffic agents. International Journal of Control, 79(4): 395–421

    Article  MATH  MathSciNet  Google Scholar 

  7. Deen, S.M. & Jayousi, R. (2006). A preference processing model for cooperative agents. Journal of Intelligence Information System, 126: 115–147

    Article  Google Scholar 

  8. DeLoach, S.A. (1999). Multiagent systems engineering: A methodology and language for designing agent systems. In: Proceedings of Agent-Oriented Information Systems (AOIS), 45–57

  9. Fan, Y., Huang, C., Wang, Y. & Zhang, L. (2005). Architecture and operational mechanism of networked manufacturing integrated platform. International Journal of Production Research, 43(12): 2615–2629

    Article  Google Scholar 

  10. Forget, P., D’Amours, S. & Frayret, J.-M. (2008). Multi-behavior agent model for planning in supply chains: an application to the lumber industry. Robotics and Computer-Integrated Manufacturing, 24(5): 664–679

    Article  Google Scholar 

  11. Kowalczyk, R., Franczyk, B., Speck, A., Braun, P., Eismann, J. & Rossak, W. (2002). Intermarket-towards intelligent mobile agent e-marketplaces. In: Proceedings of 9th Annual IEEE International Conference and Workshop on Engineering of Computer-Based Systems, 268–275

  12. Kuk, S.H., Kim, H.S., Lee, J.K., Han, S. & Park, S.W. (2008). An e-engineering framework based on service-oriented architecture and agent technologies. Computers in Industry, 59(9): 923–935

    Article  Google Scholar 

  13. Lau, J.S.K., Huang, G.Q., Mak, K.L. & Liang, L. (2005). Distributed project scheduling with information sharing in supply chains, Part I: an agent-based negotiation model. International Journal of Production Research, 43(22): 4813–4838

    Article  MATH  Google Scholar 

  14. Lau, J.S.K., Huang, G.Q., Mak, K.L. & Liang, L. (2006). Agent-based modeling of supply chains for distributed scheduling. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 136(5): 847–861

    Article  Google Scholar 

  15. Li, Y., Jian, J., Yan, R. & Liao, W. (2009). Aircraft tooling collaborative design based on multi-agent and PDM. Concurrent Engineering, 17(2): 139–146

    Article  Google Scholar 

  16. Lin, F.R. & Chang, K.Y. (2001). A multi-agent framework for automated online bargaining. IEEE Intelligent Systems, 16(4): 41–47

    Article  Google Scholar 

  17. Lu, T.P., Chang, T.M. & Yih, Y. (2005). Production control framework for supply chain management-an application in the elevator manufacturing industry. International Journal of Production Research, 43(20): 4219–4233

    Article  Google Scholar 

  18. Nwana, H., Ndumu, D., Lee, L. & Collis, J. (1999). ZEUS: a tool-kit for building distributed multi-agent systems. Journal of Applied Artificial Intelligence, 13: 187–208

    Article  Google Scholar 

  19. Rozinat, A., De Medeiros, A.K.A., Gunther, C.W., Weijters, A.J.M.M. & van der Aalst, W.M.P. (2008). The need for a process mining evaluation framework in research and practice. Lecture Notes in Computer Science, 4928: 84–89

    Article  Google Scholar 

  20. Shen, W., Hao, Q., Yoon, H.J. & Norrie, D.H. (2006). Applications of agent-based systems in intelligent manufacturing: an updated review. Advanced Engineering Informatics, 20: 415–431

    Article  Google Scholar 

  21. Schoop, R., Neubert, R. & Colombo, A.W. (2001). A multiagent-based distributed control platform for industrial flexible production systems. In: IECON’01, The 27th Annual Conference of the IEEE Industrial Electronics Society, 1: 279–284

    Google Scholar 

  22. Stadtler, H. (2005). Supply chain management and advanced planning-basics, overview and challenges. European Journal of Operational Research, 163(3): 575–588

    Article  MATH  Google Scholar 

  23. Wen, L., Wang, J. & Sun, J.G. (2006). Detecting implicit dependencies between tasks from event logs. Lecture Notes in Computer Science, 3841: 591–603

    Article  Google Scholar 

  24. Weijters, A.J.M.M. & van der Aalst, W.M.P. (2003). Rediscovering workflow models from event-based data. Integrated Computer-Aided Engineering, 10(2): 151–162

    Google Scholar 

  25. Wooldridge, M., Jennings, R. & Kinny, D. (2000). The Gaia methodology for agent-oriented analysis and design. Autonomous Agents and Multi-Agent Systems, 3: 285–312

    Article  Google Scholar 

  26. Zhang, W.J. & Xie, S.Q. (2007). Agent technology for collaborative process planning: a review. International Journal of Advanced Manufacturing Technology, 32: 315–325

    Article  Google Scholar 

  27. Zhou, M. (1993). Petri Net Synthesis for Discrete Event Control of Manufacturing Systems. Kluwer Academic Publishers, Massachusetts

    MATH  Google Scholar 

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Authors and Affiliations

  1. Depart. of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan, China

    C. Ou-Yang

  2. Depart. of Industrial Engineering and Management, Ming Chi University of Technology, Taipei, Taiwan, China

    Yeh-Chun Juan

Authors
  1. C. Ou-Yang
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  2. Yeh-Chun Juan
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Corresponding author

Correspondence to C. Ou-Yang.

Additional information

The paper was financially supported by National Science Council, Taiwan, through project No. NSC-95-2221-E-011-015

C. Ou-Yang is a professor in the Department of Industrial Management, National Taiwan University of Science and Technology (NTUST), Taiwan, China. He received his PhD degree from Dept. of Industrial and Systems Engineering, The Ohio State University. Currently, he serves as the associate dean for the school of management, and director of the graduate institute of management in NTUST. His main research interests include business process management, concurrent engineering, and collaborative engineering.

Yeh-Chun Juan currently is an associate professor of Department of Industrial Engineering and Management at Ming Chi University of Technology, Taiwan, China. He received his Ph.D. degree in Industrial Management at National Taiwan University of Science and Technology, Taiwan, China in 2003. His current research and teaching interests are in the general area of Business Process Management, Design/Supply Chain Management, Collaboration Commerce and Business Intelligence. He is a member of Electronic Business Management Society (EBMS).

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Ou-Yang, C., Juan, YC. Applying process mining approach to support the verification of a multi-agent system. J. Syst. Sci. Syst. Eng. 19, 131–149 (2010). https://doi.org/10.1007/s11518-010-5132-z

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  • DOI: https://doi.org/10.1007/s11518-010-5132-z

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