Robust Optimization Approach and Learning Based Hide-and-Seek Game for Resilient Network Design

TL;DR

This paper develops a robust optimization framework and a learning-based game for designing resilient communication networks with uncertain link lengths and regeneration costs, ensuring connectivity under worst-case scenarios.

Contribution

It introduces a novel dynamic uncertainty set for link lengths and integrates robust optimization with a learning-based hide-and-seek game for network design.

Findings

Proposed scalable solution methods outperform classical models.

Dynamic uncertainty sets improve robustness in network design.

The approach guarantees network connectivity under worst-case uncertainties.

Abstract

We study the design of resilient and reliable communication networks in which a signal can be transferred only up to a limited distance before its quality falls below an acceptable threshold. When excessive signal degradation occurs, regeneration is required through regenerators installed at selected network nodes. In this work, both network links and nodes are subject to uncertainty. The installation costs of regenerators are modeled using a budgeted uncertainty set. In addition, link lengths follow a dynamic budgeted uncertainty set introduced in this paper, where deviations may vary over time. Robust optimization seeks solutions whose performance is guaranteed under all scenarios represented by the underlying uncertainty set. Accordingly, the objective is to identify a minimum-cost subset of nodes for regenerator deployment that ensures full network connectivity, even under the worst possible realizations of uncertainty. To solve the problem, we first formulate it within a robust optimization framework, and then develop scalable solution methods based on column-and-constraint generation, Benders decomposition, and iterative robust optimization. In addition, we formulate a learning-based hide-and-seek game to further analyze the problem structure. The proposed approaches are evaluated against classical static budgeted robust models and deterministic worst-case formulations. Both theoretical analysis and computational results demonstrate the effectiveness and advantages of our methodology.