Risk Averse Shortest Paths: A Computational Study

Renaud Chicoisne (), Fernando Ordóñez () and Daniel Espinoza ()
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Renaud Chicoisne: HEC Montréal, Montréal, Québec H3T 2A7, Canada
Fernando Ordóñez: Universidad de Chile, Santiago, 8370456 Chile
Daniel Espinoza: Gurobi Inc., Santiago, Chile

INFORMS Journal on Computing, 2018, vol. 30, issue 3, 539-553

Abstract: In this work we consider the shortest path problem with uncertainty in arc lengths and convex risk measure objective. We explore efficient implementations of sample average approximation (SAA) methods to solve shortest path problems when the conditional value at risk and entropic risk measures are used and there is correlation present in the uncertain arc lengths. Our work explores the use of different decomposition techniques to achieve an efficient implementation of SAA methods for these nonlinear convex integer optimization problems. A computational study shows the effect of geometry, uncertainty correlation and variance, and risk measure parameters on efficiency and accuracy of the methods developed.

Keywords: stochastic shortest path; sample average approximation; conditional value at risk; entropic risk measure; aggregation methods (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:30:y:2018:i:3:p:539-553

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