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647. Exact and Approximate Schemes for Robust Optimization Problems with Decision Dependent Information Discovery

Invited abstract in session TC-35: Stochastic Optimization with Decision-Dependent Uncertainty, stream Stochastic, Robust and Distributionally Robust Optimization.

Tuesday, 12:30-14:00
Room: 44 (building: 303A)

Authors (first author is the speaker)

1. Rosario Paradiso
Vrije Universiteit Amsterdam
2. Angelos Georghiou
University of Cyprus
3. Said Dabia
Department of Operations Analytics, Vrije Universiteit Amsterdam
4. Denise Tönissen
Supply Chain Analytics, Vrije Universiteit Amsterdam

Abstract

Uncertain optimization problems with decision-dependent information discovery allow the decision maker to control the timing of information discovery, in contrast to the classic multistage setting where uncertain parameters are revealed sequentially based on a prescribed filtration. This problem class is useful in a wide range of applications, however, its assimilation is partly limited by the lack of efficient solution schemes. In this work, we study two-stage robust optimization problems with decision-dependent information discovery where uncertainty appears in the objective function. The paper's contributions are twofold: we develop an exact solution scheme based on a nested decomposition algorithm, and we improve upon the existing K-adaptability approximation by strengthening its formulation using techniques from the integer programming literature. We use the orienteering problem as our working example, a challenging problem from the logistics literature which naturally fits within this framework. The complex structure of the routing recourse problem forms a challenging test bed for the proposed solution schemes, in which we show that the exact solution method outperforms at times the K-adaptability approximation, however, the strengthened K-adaptability formulation can provide good quality solutions in larger instances while significantly outperforming existing approximation schemes even in the decision independent information discovery setting.

Keywords

Status: accepted

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