Speeding Up the Estimation of Expected Maximum Flows through Reliable Networks

Abstract

This paper presents a strategy for speeding up the estimation of expected maximum flows through reliable networks. Computational experiments with the strategy on three types of randomly generated networks show that it reduces the number of flow augmentations required for evaluating the states in the sample by as much as 52 per cent on average with a standard deviation of 7 per cent compared to the conventional strategy. This leads to an average time saving of about 71 per cent with a standard deviation of about 8 per cent.