Skip to main content
SpringerLink
Log in

Model-Based Multi-objective Safety Optimization

  • Conference paper
Computer Safety, Reliability, and Security (SAFECOMP 2011)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 6894))

Included in the following conference series:

Abstract

It is well-known that in many safety critical applications safety goals are antagonistic to other design goals or even antagonistic to each other. This is a big challenge for the system designers who have to find the best compromises between different goals.

In this paper, we show how model-based safety analysis can be combined with multi-objective optimization to balance a safety critical system wrt. different goals. In general the presented approach may be combined with almost any type of (quantitative) safety analysis technique. For additional goal functions, both analytic and black-box functions are possible, derivative information about the functions is not necessary. As an example, we use our quantitative model-based safety analysis in combination with analytical functions describing different other design goals. The result of the approach is a set of best compromises of possible system variants.

Technically, the approach relies on genetic algorithms for the optimization. To improve efficiency and scalability to complex systems, elaborate estimation models based on artificial neural networks are used which speed up convergence. The whole approach is illustrated and evaluated on a real world case study from the railroad domain.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abdulla, P.A., Deneux, J., Stålmarck, G., Ågren, H., Åkerlund, O.: Designing safe, reliable systems using Scade. In: Margaria, T., Steffen, B. (eds.) ISoLA 2004. LNCS, vol. 4313, pp. 115–129. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  2. Böde, E., Peikenkamp, T., Rakow, J., Wischmeyer, S.: Model based importance analysis for minimal cut sets. In: Cha, S(S.), Choi, J.-Y., Kim, M., Lee, I., Viswanathan, M. (eds.) ATVA 2008. LNCS, vol. 5311, pp. 303–317. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  3. Bozzano, M., Villafiorita, A.: Improving system reliability via model checking: the FSAP/NuSMV-SA safety analysis platform. In: Anderson, S., Felici, M., Littlewood, B. (eds.) SAFECOMP 2003. LNCS, vol. 2788, pp. 49–62. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  4. Bozzano, M., Cimatti, A., Katoen, J.-P., Nguyen, V.Y., Noll, T., Roveri, M.: Model-based codesign of critical embedded systems. In: Proceedings of ACES-MB, vol. 507, pp. 87–91. CEUR Workshop Proceedings (2009)

    Google Scholar 

  5. Bozzano, M., Cimatti, A., Katoen, J.-P., Nguyen, V.Y., Noll, T., Roveri, M.: Safety, dependability, and performance analysis of extended AADL models. The Computer Journal (2010)

    Google Scholar 

  6. Branke, J., Schmidt, C.: Faster convergence by means of fitness estimation. Soft Computing - A Fusion of Foundations, Methodologies and Applications 9, 13–20 (2005)

    Google Scholar 

  7. Branke, J., Deb, K., Dierolf, H., Osswald, M.: Finding knees in multi-objective optimization. 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.) PPSN 2004. LNCS, vol. 3242, pp. 722–731. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  8. Grunske, L., Colvin, R., Winter, K.: Probabilistic model-checking support for FMEA. In: Proceedings of the QEST. IEEE, Los Alamitos (2007)

    Google Scholar 

  9. Grunske, L.: Early quality prediction of component-based systems - a generic framework. Journal of Systems and Software 80(5), 678–686 (2007); Component-Based Software Engineering of Trustworthy Embedded Systems

    Article  Google Scholar 

  10. Güdemann, M., Ortmeier, F., Reif, W.: Computing ordered minimal critical sets. In: Proceedings of FORMS / FORMAT (2008)

    Google Scholar 

  11. Güdemann, M., Ortmeier, F.: A framework for qualitative and quantitative model-based safety analysis. In: Proceedings of HASE 2010 (2010)

    Google Scholar 

  12. Güdemann, M., Ortmeier, F.: Probabilistic model-based safety analysis. In: Proceedings of QAPL. EPTCS (2010)

    Google Scholar 

  13. Güdemann, M., Ortmeier, F.: Quantitative model-based safety analysis: A case study. In: Proceedings of SICHERHEIT. LNI (2010)

    Google Scholar 

  14. Hansson, H., Jonsson, B.: A logic for reasoning about time and reliability. Formal Aspects of Computing 6, 102–111 (1994)

    Article  MATH  Google Scholar 

  15. Katoen, J.-P., Zapreev, I.S., Hahn, E.M., Hermanns, H., Jansen, D.N.: The ins and outs of the probabilistic model checker MRMC. Performance Evaluation, Corrected Proof. 167–176 (2010) (in press)

    Google Scholar 

  16. Kletz, T.A.: Hazop and HAZAN notes on the identification and assessment of hazards. Technical report, Inst. of Chemical Engineers, Rugby, England (1986)

    Google Scholar 

  17. Klose, J., Thums, A.: The STATEMATE reference model of the reference case study ‘Verkehrsleittechnik’. Technical Report 2002-01, Universität Augsburg (2002)

    Google Scholar 

  18. Kwiatkowska, M., Norman, G., Parker, D.: Prism: Probabilistic symbolic model checker, pp. 200–204. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  19. Miettinen, K.: Some methods for nonlinear multi-objective optimization. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 1–20. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  20. Nain, P.K.S., Deb, K.: Computationally effective search and optimization procedure using coarse to fine approximations. In: Proceedings of CEC (2003)

    Google Scholar 

  21. Nissen, S.: Implementation of a fast artificial neural network library (fann). Technical report, Department of Computer Science University of Copenhagen, DIKU (2003), http://fann.sf.net

  22. Ortmeier, F., Reif, W.: Safety optimization: A combination of fault tree analysis and optimization techniques. In: Proceedings of DSN, Florence. IEEE Computer Society, Los Alamitos (2004)

    Google Scholar 

  23. Ortmeier, F., Reif, W., Schellhorn, G.: Formal safety analysis of a radio-based railroad crossing using deductive cause-consequence analysis (DCCA). In: Dal Cin, M., Kaâniche, M., Pataricza, A. (eds.) EDCC 2005. LNCS, vol. 3463, pp. 210–224. Springer, Heidelberg (2005)

    Google Scholar 

  24. Ortmeier, F., Schellhorn, G., Reif, W.: Safety optimization of a radio-based railroad crossing. In: Proceedings of FORMS / FORMAT (2004)

    Google Scholar 

  25. Ortmeier, F.: Formale Sicherheitsanalyse. Logos Verlag, Berlin (2006)

    Google Scholar 

  26. Ortmeier, F., Güdemann, M., Reif, W.: Formal failure models. In: Proceedings of DCDS. Elsevier, Amsterdam (2007)

    Google Scholar 

  27. Ortmeier, F., Schellhorn, G.: Formal Fault Tree Analysis - Practical Experiences. In: Proceedings of AVoCS (2006)

    Google Scholar 

  28. Papadopoulos, Y., Walker, M., Parker, D., Rüde, E., Hamann, R., Uhlig, A., Grätz, U., Lie, R.: Engineering failure analysis and design optimisation with hip-hops. Engineering Failure Analysis (2010)

    Google Scholar 

  29. Pasquini, A., Papadopoulos, Y., McDermid, J.: Hierarchically performed hazard origin and propagation studies. In: Felici, M., Kanoun, K., Pasquini, A. (eds.) SAFECOMP 1999. LNCS, vol. 1698, pp. 139–152. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  30. Deb, K., Pratap, A., Agarwal, S., Meyarivan T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transaction on Evolutionary Computation, 181–197 (2002)

    Google Scholar 

  31. Vesley, W., Dugan, J., Fragole, J., Minarik II, J., Railsback, J.: Fault Tree Handbook with Aerospace Applications. NASA Office of Safety and Mission Assurance (August 2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Systems in Engineering, Otto-von-Guericke University of Magdeburg, Germany

    Matthias Güdemann & Frank Ortmeier

Authors
  1. Matthias Güdemann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frank Ortmeier
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Ansaldo STS, Via Argine, 425, 80147, Napoli, Italy

    Francesco Flammini

  2. Italian Association Critical Infrastructure (AIIC), Rome, Italy

    Sandro Bologna

  3. Dipartimento di Informatica e Sistemistica, Università di Napoli Federico II, Via Claudio, 21, Napoli, Italy

    Valeria Vittorini

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Güdemann, M., Ortmeier, F. (2011). Model-Based Multi-objective Safety Optimization. In: Flammini, F., Bologna, S., Vittorini, V. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2011. Lecture Notes in Computer Science, vol 6894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24270-0_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-24270-0_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24269-4

  • Online ISBN: 978-3-642-24270-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation