Model-Based Multi-objective Safety Optimization

  • Matthias Güdemann
  • Frank Ortmeier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6894)


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.


Failure Mode Safety Analysis Fault Tree Analysis Hazard Probability Safety Optimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Matthias Güdemann
    • 1
  • Frank Ortmeier
    • 1
  1. 1.Computer Systems in EngineeringOtto-von-Guericke University of MagdeburgGermany

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