Data-driven Approximation of Distributionally Robust Chance Constraints using Bayesian Credible Intervals

TL;DR

This paper introduces a data-driven method for approximating distributionally robust chance constraints using Bayesian credible intervals, providing a flexible framework with finite-sample guarantees and applications in portfolio management and queuing systems.

Contribution

It proposes a unified Bayesian-based approach to construct ambiguity sets for distributionally robust chance constraints, applicable under various distribution assumptions.

Findings

Provides finite-sample probability guarantees for solutions.

Demonstrates effectiveness through portfolio and queuing system experiments.

Offers a flexible, unified framework for uncertainty set construction.

Abstract

The non-convexity and intractability of distributionally robust chance constraints make them challenging to cope with. From a data-driven perspective, we propose formulating it as a robust optimization problem to ensure that the distributionally robust chance constraint is satisfied with high probability. To incorporate available data and prior distribution knowledge, we construct ambiguity sets for the distributionally robust chance constraint using Bayesian credible intervals. We establish the congruent relationship between the ambiguity set in Bayesian distributionally robust chance constraints and the uncertainty set in a specific robust optimization. In contrast to most existent uncertainty set construction methods which are only applicable for particular settings, our approach provides a unified framework for constructing uncertainty sets under different marginal distribution assumptions, thus making it more flexible and widely applicable. Additionally, under the concavity assumption, our method provides strong finite sample probability guarantees for optimal solutions. The practicality and effectiveness of our approach are illustrated with numerical experiments on portfolio management and queuing system problems. Overall, our approach offers a promising solution to distributionally robust chance constrained problems and has potential applications in other fields.