Pareto Adaptive Robust Optimality via a Fourier-Motzkin Elimination Lens

TL;DR

This paper introduces Pareto Adaptive Robust Optimality (PARO) for linear Adaptive Robust Optimization, proving its existence, proposing methods to find it, and demonstrating its practical benefits through numerical experiments.

Contribution

It formalizes PARO for ARO, shows its advantages over existing solutions, and employs Fourier-Motzkin Elimination for analysis and proof techniques.

Findings

PARO solutions exist for linear ARO problems.

Proposed approaches effectively find or approximate PARO solutions.

Numerical results show PARO solutions outperform traditional methods.

Abstract

We formalize the concept of Pareto Adaptive Robust Optimality (PARO) for linear Adaptive Robust Optimization (ARO) problems. A worst-case optimal solution pair of here-and-now decisions and wait-and-see decisions is PARO if it cannot be Pareto dominated by another solution, i.e., there does not exist another such pair that performs at least as good in all scenarios in the uncertainty set and strictly better in at least one scenario. We argue that, unlike PARO, extant solution approaches -- including those that adopt Pareto Robust Optimality from static robust optimization -- could fail in ARO and yield solutions that can be Pareto dominated. The latter could lead to inefficiencies and suboptimal performance in practice. We prove the existence of PARO solutions, and present particular approaches for finding and approximating such solutions. We present numerical results for a facility location problem that demonstrate the practical value of PARO solutions. Our analysis of PARO relies on an application of Fourier-Motzkin Elimination as a proof technique. We demonstrate how this technique can be valuable in the analysis of ARO problems, besides PARO. In particular, we employ it to devise more concise and more insightful proofs of known results on (worst-case) optimality of decision rule structures.