Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM)
236 Biased Monte Carlo Simulation Technique for Multi-State Network Unreliability Assessment (PSAM-0070)
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The quantitative assessment of the reliability of network systems can be a quite difficult and computationally expensive problem in practice. In this respect, Monte Carlo simulation offers a valuable tool for capturing the complex stochastic behavior of distributed, interconnected systems.
To reduce the computational burden associated to the simulation, it is possible to resort to biasing techniques. These techniques have already been proven successful for binary network systems, in which the arcs and the nodes can stay in only two states, either functioning or failed.
In this paper, a biasing method is proposed for improving the efficiency of the unreliability estimate by Monte Carlo simulation of complex multi-state network systems, in which the arcs and the nodes can stay in various states of different performance. The biasing is founded on a sampling strategy tailored to encourage the multi-state system to enter failed configurations with respect to the required demand at the network target node. This is achieved by forcing the arcs to visit their lower performance states.
The efficiency of the method is tested on a literature case study and a sensitivity analysis is carried out with respect to the parameter controlling the intensity of the bias.