Paper Info
Title
Model-based multi-objective safety optimization
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.
Year
DOI
Venue
2011
10.1007/978-3-642-24270-0_31
SAFECOMP
Keywords
Field
DocType
model-based safety analysis,complex system,whole approach,safety critical system wrt,model-based multi-objective safety optimization,safety analysis technique,quantitative model-based safety analysis,safety critical applications safety,best compromise,different goal,design goal
Complex system,Convergence (routing),Computer science,Analytic function,Fault tree analysis,Artificial neural network,Reliability engineering,Genetic algorithm,Speedup,Scalability
Conference
Citations 
PageRank 
References 
6
0.55
22
Authors
2
Name
Order
Citations
PageRank
Matthias Güdemann112811.15
Frank Ortmeier238647.95