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Related Experiment Video

Updated: Aug 31, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Adaptive algorithm for dependent infrastructure network restoration in an imperfect information sharing environment.

Alireza Rangrazjeddi¹, Andrés D González¹, Kash Barker¹

¹School of Industrial and Systems Engineering, University of Oklahoma, Norman, Oklahoma, United States of America.

|August 24, 2022

Summary

This summary is machine-generated.

This study introduces a machine learning algorithm for critical infrastructure resilience planning amid network interdependencies and uncertain information. The adaptive approach effectively integrates predictions of other decision-makers

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Area of Science:

Engineering
Computer Science
Operations Research

Background:

Critical infrastructure networks are essential for societal function, yet their resilience is challenged by interdependencies.
Decision-making for resilience is complex due to competing interests and uncertain information among interdependent network operators.

Purpose of the Study:

To develop an adaptive machine learning algorithm for critical infrastructure resilience planning.
To address challenges posed by network interdependencies and imperfect information sharing environments.

Main Methods:

An adaptive algorithm using machine learning was developed to predict other decision-makers' behavior.
The algorithm was integrated into an interdependent network restoration planning problem under imperfect information.
Performance was evaluated against the optimal solution and a heuristic method.

Main Results:

The proposed algorithm achieved results insignificantly different from the optimal solution across various network scenarios.
Comparison with a heuristic method showed the algorithm's effectiveness in incomplete information environments.
The algorithm demonstrates robust performance in complex, interdependent network restoration planning.

Conclusions:

The adaptive machine learning algorithm offers a viable solution for enhancing critical infrastructure resilience.
The approach effectively manages decision-making complexities in interdependent networks with uncertain information.
This method provides a practical tool for optimizing restoration planning in real-world critical infrastructure systems.