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.
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Nikolova, E. (2010). High-Performance Heuristics for Optimization in Stochastic Traffic Engineering Problems. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2009. Lecture Notes in Computer Science, vol 5910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12535-5_41
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DOI: https://doi.org/10.1007/978-3-642-12535-5_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12534-8
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