The robust selection problem with information discovery

TL;DR

This paper investigates a multi-stage robust selection problem with information discovery, proposing algorithms and reformulations for special cases and demonstrating their effectiveness through numerical experiments.

Contribution

It introduces a novel multi-stage robust selection framework with queries, providing polynomial algorithms for special cases and linear programming reformulations for constraint uncertainty.

Findings

NP-hardness of the general problems

Polynomial algorithms for specific query sets

Linear programming reformulation for constraint uncertainty

Abstract

We explore a multiple-stage variant of the min-max robust selection problem with budgeted uncertainty that includes queries. First, one queries a subset of items and gets the exact values of their uncertain parameters. Given this information, one can then choose the set of items to be selected, still facing uncertainty on the unobserved parameters. In this paper, we study two specific variants of this problem. The first variant considers objective uncertainty and focuses on selecting a single item. The second variant considers constraint uncertainty instead, which means that some selected items may fail. We show that both problems are NP-hard in general. We also propose polynomial-time algorithms for special cases of the sets of items that can be queried. For the problem with constraint uncertainty, we also show how the objective function can be expressed as a linear program, leading to a mixed-integer linear programming reformulation for the general case. We illustrate the performance of this formulation using numerical experiments.