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

2026-02-12

AI Summary

This work addresses the challenge of maintaining network-wide connectivity in communication networks where link lengths vary over time and node connectivity is subject to uncertainty. To ensure robustness under worst-case scenarios while minimizing regenerator deployment costs, the paper proposes a novel robust optimization approach. Its key innovation lies in the construction of a dynamic budget uncertainty set that effectively captures temporal and structural uncertainties, combined with an integrated solution framework leveraging column-and-constraint generation, Benders decomposition, and a learning-enhanced hide-and-seek game to improve both model adaptability and computational efficiency. Theoretical analysis and extensive experiments demonstrate that the proposed method significantly outperforms conventional static robust models and deterministic worst-case approaches, achieving guaranteed connectivity with substantially reduced deployment costs.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• resilient network design
• signal regeneration
• uncertainty
• robust optimization
• network connectivity

Innovation

• robust optimization
• dynamic budgeted uncertainty
• regenerator placement
• hide-and-seek game
• resilient network design