Information Systems Simulation for Performance Evaluation - Application in Aircraft Maintenance

  • Yinling LiuEmail author
  • Tao Wang
  • Haiqing Zhang
  • Vincent Cheutet
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 540)


In the design phase, an effective performance evaluation on the information system model for aircraft maintenance helps better iteratively refine the system design. Although many works have emerged to achieve this aim, complex systems are hard to be completely described and the consideration of real-world processes is missing. Thus, how to investigate the system performance with consideration of the real-world aircraft maintenance is our focus. From our point of view, an agent-based simulation modeling is a promising approach to overcome such problem. In this paper, we develop an agent-based simulation model for the whole maintenance process, focusing on cooperation among sub business processes. This model describes the flight process, cooperation between stakeholders and failure repairs, where scheduled/unscheduled maintenance and with/without uncertain event scenarios have been addressed. The performance evaluation of this model is based on the analysis of impacts of key factors on the airline’s service level and maintenance cost.


Agent-based simulation modeling Information system Business process Civil aircraft maintenance 



This research work is supported by the China Scholarship Council.


  1. 1.
    Liu, Y., Wang T., Cheutet V., Zhang H.: An architecture modeling methodology for aircraft maintenance service system. In: The 24th STP Conference (2017)Google Scholar
  2. 2.
    Liu, Y., Wang T., Cheutet V., Zhang H.: Towards standards analysis and application in process of aircraft maintenance repair overhaul. In: IFAC World Congress, Toulouse, France (2017)Google Scholar
  3. 3.
    Alrabghi, A., Tiwari, A.: A novel approach for modeling complex maintenance systems using discrete event simulation. Reliab. Eng. Syst. Saf. 154, 160–170 (2016). Scholar
  4. 4.
  5. 5.
    Bonabeau, E.: Predicting the unpredictable. Harv. Bus. Rev. 80, 109–116 (2002)Google Scholar
  6. 6.
    Jennings, N.R.: On agent-based software engineering. Artif. Intell. 117(2), 277–296 (2000). Scholar
  7. 7.
    Kang, K., Gue, K., Eaton, D.R.: Cycletime reduction for naval aviation deports. In: Proceedings of the 30th Conference on Winter Simulation, pp. 907–914 (1998)Google Scholar
  8. 8.
    Balaban, H.S., Brigantic, R.T., Wright, S.A., Papatyi, A.F.: A simulation approach to estimating aircraft mission capable rates for the United States air force. In: Proceedings of the 2000 Winter Simulation Conference (2000)Google Scholar
  9. 9.
    Mattila, V., Virtanen, K., Raivio, T.: A simulation model for aircraft maintenance in an uncertain operational environment. In: Proceedings of 17th European Simulation Multiconference, Nottingham, England, pp. 456–461 (2003)Google Scholar
  10. 10.
    MacKenzie, A., Miller, J.O., Hill, R.R.: Application of agent based modelling to aircraft maintenance manning and sortie generation. Simul. Model. Pract. Theory 20, 89–98 (2012). Scholar
  11. 11.
    Datta, P.P., Srivastava, A., Roy, R.: A simulation study on maintainer resource utilization of a fast jet aircraft maintenance line under availability contract. Comput. Ind. 64, 543–555 (2013). Scholar
  12. 12.
    Lee, L.H., Chew, E.P., Teng, S., Chen, Y.: Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem. Eur. J. Oper. Res. 189, 476–491 (2008). Scholar
  13. 13.
    Van den Bergh, J., De Bruecker, P., Belien, J., De Boeck, L., Demeulemeester, E.: A three-stage approach for aircraft line maintenance personnel rostering using MIP, discrete event simulation and DEA. Expert. Syst. Appl. 40(7), 2659–2668 (2013). Scholar
  14. 14.
    Regattieri, A., Giazzi, A., Gamberi, M., Gamberini, R.: An innovative method to optimize the maintenance policies in an aircraft: general framework and case study. J. Air Transp. Manag. 44, 8–20 (2015). Scholar
  15. 15.
    Passenier, D., Mols, C., Bím, J., Sharpanskykh, A.: Modeling safety culture as a socially emergent phenomenon: a case study in aircraft maintenance. Comput. Math. Organ. Theory 22, 487–520 (2016). Scholar
  16. 16.
    Hamilton, S., Chervany, N.L.: Evaluating information system effectiveness-Part I: comparing evaluation approaches. MIS Q. 5, 55–69 (1981)CrossRefGoogle Scholar
  17. 17.
    Petter, S., DeLone, W., McLean, E.: Measuring information systems success: models, dimensions, measures, and interrelationships. Eur. J. Inf. Syst. 17(3), 236–263 (2008)CrossRefGoogle Scholar
  18. 18.
    Petter, S., DeLone, W., McLean, E.R.: The past, present, and future of “IS Success”. Eur. J. Inf. Syst. 13(5), 341 (2012)Google Scholar
  19. 19.
    Khodakarami, F., Chan, Y.E.: Exploring the role of customer relationship management (CRM) systems in customer knowledge creation. Inf. Manag. 51(1), 27–42 (2014). Scholar
  20. 20.
    Moyaux, T., Chaib-draa, B., D’Amours, S.: Supply chain management and multiagent systems: an overview. In: Chaib-draa, B., Müller, J.P. (eds.) Multiagent Based Supply Chain Management, pp. 1–27. Springer, Berlin (2006). Scholar

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  • Yinling Liu
    • 1
    Email author
  • Tao Wang
    • 2
  • Haiqing Zhang
    • 3
  • Vincent Cheutet
    • 1
  1. 1.Université de Lyon, INSA Lyon, Laboratoire DISP (EA4570)VilleurbanneFrance
  2. 2.Université de Lyon, Université Jean Monnet, Laboratoire DISP (EA4570)VilleurbanneFrance
  3. 3.School of Software EngineeringChengdu University Information TechnologyChengduChina

Personalised recommendations