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Robust combinatorial optimization with variable cost uncertainty

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  • Poss, Michael

Abstract

We present in this paper a new model for robust combinatorial optimization with cost uncertainty that generalizes the classical budgeted uncertainty set. We suppose here that the budget of uncertainty is given by a function of the problem variables, yielding an uncertainty multifunction. The new model is less conservative than the classical model and approximates better Value-at-Risk objective functions, especially for vectors with few non-zero components. An example of budget function is constructed from the probabilistic bounds computed by Bertsimas and Sim. We provide an asymptotically tight bound for the cost reduction obtained with the new model. We turn then to the tractability of the resulting optimization problems. We show that when the budget function is affine, the resulting optimization problems can be solved by solving n+1 deterministic problems. We propose combinatorial algorithms to handle problems with more general budget functions. We also adapt existing dynamic programming algorithms to solve faster the robust counterparts of optimization problems, which can be applied both to the traditional budgeted uncertainty model and to our new model. We evaluate numerically the reduction in the price of robustness obtained with the new model on the shortest path problem and on a survivable network design problem.

Suggested Citation

  • Poss, Michael, 2014. "Robust combinatorial optimization with variable cost uncertainty," European Journal of Operational Research, Elsevier, vol. 237(3), pages 836-845.
  • Handle: RePEc:eee:ejores:v:237:y:2014:i:3:p:836-845
    DOI: 10.1016/j.ejor.2014.02.060
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    References listed on IDEAS

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    1. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    2. F. Benjamin Zhan & Charles E. Noon, 1998. "Shortest Path Algorithms: An Evaluation Using Real Road Networks," Transportation Science, INFORMS, vol. 32(1), pages 65-73, February.
    3. Quentin Botton & Bernard Fortz & Luis Gouveia & Michael Poss, 2013. "Benders Decomposition for the Hop-Constrained Survivable Network Design Problem," INFORMS Journal on Computing, INFORMS, vol. 25(1), pages 13-26, February.
    4. Chungmok Lee & Kyungsik Lee & Kyungchul Park & Sungsoo Park, 2012. "Technical Note---Branch-and-Price-and-Cut Approach to the Robust Network Design Problem Without Flow Bifurcations," Operations Research, INFORMS, vol. 60(3), pages 604-610, June.
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    Cited by:

    1. Artur Alves Pessoa & Michael Poss & Ruslan Sadykov & François Vanderbeck, 2021. "Branch-Cut-and-Price for the Robust Capacitated Vehicle Routing Problem with Knapsack Uncertainty," Operations Research, INFORMS, vol. 69(3), pages 739-754, May.
    2. Enrico Bartolini & Dominik Goeke & Michael Schneider & Mengdie Ye, 2021. "The Robust Traveling Salesman Problem with Time Windows Under Knapsack-Constrained Travel Time Uncertainty," Transportation Science, INFORMS, vol. 55(2), pages 371-394, March.
    3. Mehdi Ansari & Juan S. Borrero & Leonardo Lozano, 2023. "Robust Minimum-Cost Flow Problems Under Multiple Ripple Effect Disruptions," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 83-103, January.
    4. Claire Nicolas & Stéphane Tchung-Ming & Emmanuel Hache, 2016. "Energy transition in transportation under cost uncertainty, an assessment based on robust optimization," Working Papers hal-02475943, HAL.
    5. Matthews, Logan R. & Gounaris, Chrysanthos E. & Kevrekidis, Ioannis G., 2019. "Designing networks with resiliency to edge failures using two-stage robust optimization," European Journal of Operational Research, Elsevier, vol. 279(3), pages 704-720.
    6. Fliedner, Thomas & Liesiö, Juuso, 2016. "Adjustable robustness for multi-attribute project portfolio selection," European Journal of Operational Research, Elsevier, vol. 252(3), pages 931-946.
    7. Di Puglia Pugliese, Luigi & Ferone, Daniele & Macrina, Giusy & Festa, Paola & Guerriero, Francesca, 2023. "The crowd-shipping with penalty cost function and uncertain travel times," Omega, Elsevier, vol. 115(C).
    8. Annette M. C. Ficker & Frits C. R. Spieksma & Gerhard J. Woeginger, 2018. "Robust balanced optimization," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 6(3), pages 239-266, September.

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