A Robust Optimization Approach for Regenerator Placement in Fault-Tolerant Networks Under Discrete Cost Uncertainty

2026-02-11

AI Summary

This study addresses the regenerator deployment problem in fault-tolerant optical networks under link failures and signal attenuation constraints, explicitly incorporating discrete uncertainty in regenerator installation costs—a consideration previously unexplored in the literature. The authors propose a robust optimization approach that guarantees full network connectivity under the worst-case cost scenario. Building upon a flow- and cut-based integer programming framework, they develop two exact solution algorithms that integrate robust optimization theory to handle cost uncertainty. Theoretical analysis and computational experiments demonstrate that the proposed method efficiently computes optimal deployment strategies across diverse uncertainty scenarios, effectively balancing cost control with guaranteed network connectivity.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

We focus on robust, survivable communication networks, where network links and nodes are affected by an uncertainty set. In this sense, any network links might fail. Besides, a signal can only travel a maximum distance before its quality falls below a certain threshold, necessitating its regeneration by regenerators installed at network nodes. In addition, the price of installing and maintaining regenerators belongs to a discrete uncertainty set. Robust optimization seeks a solution with guaranteed performance against all scenarios modeled in an uncertainty set. Thus, the problem is to find a subset of nodes with minimum cost for the placement of the regenerator, ensuring that all nodes can communicate even if a subset of network links fails. To solve the problem optimally, we propose two solution approaches, including one flow-based and one cut-based integer programming formulation, as well as their iterative exact method. Our theoretical and experimental results show the effectiveness of our methods.

Problem

• regenerator placement
• fault-tolerant networks
• discrete cost uncertainty
• robust optimization
• survivable communication

Innovation

• robust optimization
• regenerator placement
• discrete cost uncertainty
• survivable networks
• integer programming