Optimizing Network Reliability in Dynamic or Rapidly-Changing Topologies

Abstract

With communication, power and transportation networks facing ever-greater dynamics, optimizing reliability poses significant modeling and computational challenges. Most existing reliability optimization research assumes static topologies with predefined failure probabilities and connectivity demands. This work puts forth a time-varying mathematical program to maximize expected network reliability under shifting topological uncertainties.

Sets, parameters, decision variables and constraints are defined as functions of time to capture variability in links, capacities, risks and demands. The formulation adapts to detected changes through re-optimization triggered by model error thresholds. Approximation techniques based on constraint sampling and decomposition address solve efficiency for large fluid networks.

Evaluations on simulated dynamic test cases demonstrate superior reliability versus periodic and static optimization approaches while fulfilling budget limits. Accuracy metrics assess model fidelity over increasing volatility levels. Implementation case studies exhibit optimized resilience in software-defined communication architectures, smart grid reconfiguration, and adaptive transportation maintenance scenarios.

The mathematical programming foundation provides a pathway to achieve connectivity resilience for critical infrastructure networks facing intensifying dynamics. The integration of optimization, prediction and adaptive response provides a paradigm for decision making under modern uncertain conditions.