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Article Dans Une Revue INFORMS Journal on Computing Année : 2023

Optimization problems in graphs with locational uncertainty

Résumé

Many discrete optimization problems amount to selecting a feasible subgraph of least weight. We consider in this paper the context of spatial graphs where the positions of the vertices are uncertain and belong to known uncertainty sets. The objective is to minimize the sum of the distances in the chosen subgraph for the worst positions of the vertices in their uncertainty sets. We first prove that these problems are NP-hard even when the feasible subgraphs consist either of all spanning trees or of all s − t paths. Given this hardness, we propose an exact solution algorithm combining integer programming formulations with a cutting plane algorithm, identifying the cases where the separation problem can be solved efficiently. We also propose a conservative approximation and show its equivalence to the affine decision rule approximation in the context of Euclidean distances. We compare our algorithms to three deterministic reformulations on instances inspired by the scientific literature for the Steiner tree problem and a facility location problem.
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Dates et versions

hal-03331166 , version 1 (01-09-2021)
hal-03331166 , version 2 (03-09-2021)
hal-03331166 , version 3 (04-05-2022)
hal-03331166 , version 4 (27-09-2022)
hal-03331166 , version 5 (20-12-2023)

Identifiants

Citer

Marin Bougeret, Jérémy Omer, Michael Poss. Optimization problems in graphs with locational uncertainty. INFORMS Journal on Computing, 2023, 35 (3), pp.578-592. ⟨10.1287/ijoc.2023.1276⟩. ⟨hal-03331166v5⟩
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