Stochastic Shortest Paths and Risk Measures

We consider three shortest path problems in directed graphs with random arc lengths. For the first and the second problems, a risk measure is involved. While the first problem consists in finding a path minimizing this risk measure, the second one consists in finding a path minimizing a deterministic cost, while satisfying a constraint on the risk measure. We propose algorithms solving these problems for a wide range of risk measures, which includes among several others the CV aR and the probability of being late. Their performances are evaluated through experiments. One of the key elements in these algorithms is the use of stochastic lower bounds that allow to discard partial solutions. Good stochastic lower bounds are provided by the so-called Stochastic On Time Arrival Problem. This latter problem is the third one studied in this paper and we propose a new and very efficient algorithm solving it. Complementary discussions on the complexity of the problems are also provided.