TY - JOUR

T1 - A biobjective approach to recoverable robustness based on location planning

AU - Carrizosa, Emilio

AU - Goerigk, Marc

AU - Schöbel, Anita

N1 - This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 261, 2, 2017 DOI: 10.1016/j.ejor.2017.02.014

PY - 2017/9/1

Y1 - 2017/9/1

N2 - Finding robust solutions of an optimization problem is an important issue in practice, and various concepts on how to define the robustness of a solution have been suggested. The idea of recoverable robustness requires that a solution can be recovered to a feasible one as soon as the realized scenario becomes known. The usual approach in the literature is to minimize the objective function value of the recovered solution in the nominal or in the worst case.As the recovery itself is also costly, there is a trade-off between the recovery costs and the solution value obtained; we study both, the recovery costs and the solution value in the worst case in a biobjective setting.To this end, we assume that the recovery costs can be described by a metric. We show that in this case the recovery robust problem can be reduced to a location problem.We show how weakly Pareto efficient solutions to this biobjective problem can be computed by minimizing the recovery costs for a fixed worst-case objective function value and present approaches for the case of linear and quasiconvex problems for finite uncertainty sets. We furthermore derive cases in which the size of the uncertainty set can be reduced without changing the set of Pareto efficient solutions.

AB - Finding robust solutions of an optimization problem is an important issue in practice, and various concepts on how to define the robustness of a solution have been suggested. The idea of recoverable robustness requires that a solution can be recovered to a feasible one as soon as the realized scenario becomes known. The usual approach in the literature is to minimize the objective function value of the recovered solution in the nominal or in the worst case.As the recovery itself is also costly, there is a trade-off between the recovery costs and the solution value obtained; we study both, the recovery costs and the solution value in the worst case in a biobjective setting.To this end, we assume that the recovery costs can be described by a metric. We show that in this case the recovery robust problem can be reduced to a location problem.We show how weakly Pareto efficient solutions to this biobjective problem can be computed by minimizing the recovery costs for a fixed worst-case objective function value and present approaches for the case of linear and quasiconvex problems for finite uncertainty sets. We furthermore derive cases in which the size of the uncertainty set can be reduced without changing the set of Pareto efficient solutions.

KW - Robustness & sensitivity analysis

KW - Robust optimization

KW - Location planning

KW - Biobjective optimization

U2 - 10.1016/j.ejor.2017.02.014

DO - 10.1016/j.ejor.2017.02.014

M3 - Journal article

VL - 261

SP - 421

EP - 435

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 2

ER -