Stochastic Optimization and Learning for Two-Stage Supplier Problems

TL;DR

This paper develops stochastic optimization algorithms for two-stage supplier clustering problems with radius-based constraints, including inhomogeneous demands and additional decision constraints, using a novel scenario-discarding SAA method for general distributions.

Contribution

It introduces a scenario-discarding SAA approach tailored for radius-based clustering with stochastic demands and complex constraints, applicable under black-box distribution access.

Findings

Algorithms for polynomial scenarios setting

Scenario-discarding SAA method for general distributions

Effective handling of inhomogeneous radius demands

Abstract

The main focus of this paper is radius-based (supplier) clustering in the two-stage stochastic setting with recourse, where the inherent stochasticity of the model comes in the form of a budget constraint. In addition to the standard (homogeneous) setting where all clients must be within a distance $R$ of the nearest facility, we provide results for the more general problem where the radius demands may be inhomogeneous (i.e., different for each client). We also explore a number of variants where additional constraints are imposed on the first-stage decisions, specifically matroid and multi-knapsack constraints, and provide results for these settings. We derive results for the most general distributional setting, where there is only black-box access to the underlying distribution. To accomplish this, we first develop algorithms for the polynomial scenarios setting; we then employ a novel scenario-discarding variant of the standard Sample Average Approximation (SAA) method, which crucially exploits properties of the restricted-case algorithms. We note that the scenario-discarding modification to the SAA method is necessary in order to optimize over the radius.