Combinatorial Robust Optimization with Decision-Dependent Information Discovery and Polyhedral Uncertainty - Institut de Recherche Mathématiques de Rennes
Pré-Publication, Document De Travail Année : 2023

Combinatorial Robust Optimization with Decision-Dependent Information Discovery and Polyhedral Uncertainty

Résumé

Given a nominal combinatorial optimization problem, we consider a robust two-stages variant with polyhedral cost uncertainty, called DDID. In the first stage, DDID selects a subset of uncertain cost coefficients to be observed, and in the second-stage, DDID selects a solution to the nominal problem, where the remaining cost coefficients are still uncertain. Given a compact linear programming formulation for the nominal problem, we provide a mixed-integer linear programming (MILP) formulation for DDID. The MILP is compact if the number of constraints describing the uncertainty polytope other than lower and upper bounds is constant. In that case, the formulation leads to polynomial-time algorithms for DDID when the number of possible observations is polynomially bounded. We extend this formulation to more general nominal problems through column generation and constraint generation algorithms. We illustrate our reformulations and algorithms numerically on the selection problem, the orienteering problem, and the spanning tree problem.
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Dates et versions

hal-04097679 , version 1 (15-05-2023)
hal-04097679 , version 2 (05-09-2023)
hal-04097679 , version 3 (22-12-2023)
hal-04097679 , version 4 (19-06-2024)

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  • HAL Id : hal-04097679 , version 3

Citer

Jérémy Omer, Michael Poss. Combinatorial Robust Optimization with Decision-Dependent Information Discovery and Polyhedral Uncertainty. 2023. ⟨hal-04097679v3⟩

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