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Risk Averse Shortest Paths: A Computational Study

Author

Listed:
  • Renaud Chicoisne

    (HEC Montréal, Montréal, Québec H3T 2A7, Canada)

  • Fernando Ordóñez

    (Universidad de Chile, Santiago, 8370456 Chile)

  • Daniel Espinoza

    (Gurobi Inc., Santiago, Chile)

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.

Suggested Citation

  • Renaud Chicoisne & Fernando Ordóñez & Daniel Espinoza, 2018. "Risk Averse Shortest Paths: A Computational Study," INFORMS Journal on Computing, INFORMS, vol. 30(3), pages 539-553, August.
    • Handle: RePEc:inm:orijoc:v:30:y:2018:i:3:p:539-553
    DOI: 10.1287/ijoc.2017.0795
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    References listed on IDEAS

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    Cited by:

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    2. Renaud Chicoisne, 2023. "Computational aspects of column generation for nonlinear and conic optimization: classical and linearized schemes," Computational Optimization and Applications, Springer, vol. 84(3), pages 789-831, April.

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