High-Performance heuristics for optimization in stochastic traffic engineering problems

Authors:
Evdokia Nikolova
Affiliations:
Massachusetts Institute of Technology, Cambridge, MA
Venue:
LSSC'09 Proceedings of the 7th international conference on Large-Scale Scientific Computing
Year:
2009

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Abstract

We consider a stochastic routing model in which the goal is to find the optimal route that incorporates a measure of risk The problem arises in traffic engineering, transportation and even more abstract settings such as task planning (where the time to execute tasks is uncertain), etc The stochasticity is specified in terms of arbitrary edge length distributions with given mean and variance values in a graph The objective function is a positive linear combination of the mean and standard deviation of the route Both the nonconvex objective and exponentially sized feasible set of available routes present a challenging optimization problem for which no efficient algorithms are known In this paper we evaluate the practical performance of algorithms and heuristic approaches which show very promising results in terms of both running time and solution accuracy.