Improved Sampling Plans for Combinatorial Invariants of Coherent Systems

PROJECT TITLE :

Improved Sampling Plans for Combinatorial Invariants of Coherent Systems

ABSTRACT:

Terminal network reliability issues appear in many real-life applications, like transportation grids, social and computer networks, Communication systems, etc. During this paper, we concentrate on monotone binary systems with identical part reliabilities. The reliability of such systems depends only on the amount of failure sets of all potential sizes, which is a vital system invariant. For massive problems, no analytical solution for calculating this invariant in an exceedingly cheap time is understood to exist, and one should depend on totally different approximation techniques. An example of such a method is Permutation Monte Carlo. It's known that this straightforward plan isn't sufficient for adequate estimation of network reliability because of the rare-event problem. As another, we tend to propose a different sampling strategy that is primarily based on the recently pioneered Stochastic Enumeration algorithm for tree value estimation. We have a tendency to show that, thanks to its built-in splitting mechanism, this methodology is ready to deliver accurate results whereas using a relatively modest sample size. Moreover, our numerical results indicate that the proposed sampling theme is capable of solving problems that are so much beyond the reach of the simple Permutation Monte Carlo approach.

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