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Critical Infrastructure Resilience Collaboration & Assessment (CIRCA)

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MCBH K-Bay MCRD Parris Island NAVSTA Newport

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Sponsor: Office of the Secretary of Defense (OSD) Strategic Environmental Research and Development Program (SERDP)


Project Abstract: The purpose of this project is to develop methods that measure worst-case disruptions across interdependent infrastructure systems on US Department of Defense (DoD) military installations and to create models that support DoD infrastructure planning and management. The extensive damages experienced in the wake of disasters like Hurricane Florence at Marine Corps Base Camp Lejeune revealed two important deficiencies in DoD infrastructure planning and recovery: (1) military installations lack models to measure losses resulting from interdependent failures across infrastructure (e.g., water and electricity), and (2) military leadership lacks methods to incorporate compound threats into infrastructure investment plans. 


Technical Approach: This project addresses deficiencies in infrastructure planning and recovery by applying modeling methods used to identify worst-case losses in national infrastructure systems (e.g., the US power grid) to interdependent systems on military installations. Specifically, this project centers on advancing established operations research models for worst-case disruptions called “attacker-defender” models. Research activities include the development of modeling architectures for interdependent infrastructure systems and novel attack scenarios to identify worst-case disruptions to compound threats. Modeling architectures and methods will be used to design a new mission dependency index that embeds the interruptibility, relocateability, and replaceability of interdependent assets. Finally, we will develop case studies that identify vulnerabilities and optimal protection capabilities for military installations and inform long-term investment and planning.


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Project Members

Naval Postgraduate School

Dr. Daniel Eisenberg, Principal Investigator

Dr. David Alderson

Michelle Louise Hancock

Jacob Wigal


University of Rhode Island

Austin Becker


University of Maryland

Allison Coffey Reilly


US Department of Energy

Jennifer DeCesaro

Johanna Zetterberg


Converge Strategies, LLC

Michael Wu

Meredith Pringle

Adair Douglas


Energetics Technology Center

Joseph Sanchez

Installation Resilience Students


Student Theses

Mission-Informed Evacuation Models for Naval Station Newport and Aquidneck Island

LCDR Amanda Jones | M.S. Thesis in Operations Research (September 2021)


During major hurricanes, key roads and facilities for military missions can become disrupted and unreachable. This is compounded with local evacuation orders that require nearby communities and military personnel to quickly leave the area, leading to major traffic jams that further disrupt operations on and off base. In this work, we will develop a model for optimal evacuation for Naval Station Newport and surrounding communities on Aquidneck Island. The goal is to develop plans that minimize the travel time for local communities to evacuate while maximizing mission assurance for key facilities and emergency response activities that will remain on the island. This work is in collaboration with the Military Installation Resilience Review (MIRR) led by Dr. Austin Becker at the University of Rhode Island.


Overcoming Flaws in the Mission Dependency Index (MDI) with Network Flow Analysis

LCDR Aaron Fish | M.S. Thesis in Operations Research (March 2021)


The Mission Dependency Index (MDI) is a metric used by all U.S. military services for guiding operations, management, and funding decisions for facilities at military installations. Despite its broad adoption, several studies on MDI suggest it may have flaws that limit its efficacy. We present the first rigorous technical analysis of MDI to how its flaws impact decisions and determine ways to overcome them. We develop a formal mathematical definition of MDI based on multilayer networks that supports reproducible models and formal analysis of the MDI calculation process used in the U.S. Navy. Based on our multilayer formalism, we define three technical problems with MDI methods not previously discussed in the literature. We also develop a new model for calculating MDI based on network flow analysis that overcomes these problems. Overall, we answer the following questions:

  • What are the mathematical flaws in MDI as applied in the U.S. Navy?
  • How can we overcome these flaws with network flow models?


Developing a Framework for Analyzing the Resilience of Forward Expeditionary Port Refueling Infrastructure

LT Daniel Pulliam | M.S. Thesis in Operations Research (March 2021)


The U.S. Navy (USN) relies on ports to enable operations and project power, but many of our ports remain vulnerable to attack and natural disaster. To manage future conflict, the USN must plan for port resilience and develop resilience enabling technologies that support ship refueling operations. We develop a framework and model capable of studying refueling at ports before and after disruptions. Our framework adapts standard tools for discrete event simulation of ship arrival and refueling, and we demonstrate its use for a simple port. Our methods also enable the analysis of resilience technologies currently being developed by the USN. We study two USN technologies, one that enables fast port recovery and one that enables extended port operations, but does not speed up recovery. We find both technologies capable of providing resilience to ports in their own unique way. Based on our analysis, we provide recommendations for how the USN should deploy both technologies that enable efficient acquisition and port resilience. This work has direct impact on USN refueling operations and has won multiple awards for its practical and effective analysis, including:

  • Military Operations Research Society (MORS) Stephen A. Tisdale Graduate Research Award
  • The Surface Navy Association’s Award for Excellence in Surface Warfare Research


Building Resilience within DoD Microgrids by Considering Human Factors in Recovery Procedures

LT Marci Hester-Dudley | M.S. Thesis in Systems Engineering (March 2021)


Microgrid research and improvements for DoD installations focus on measuring system robustness and benefits, but have not measured how their use affects other aspects of resilience. One important gap is a lack of analysis on how increased system complexity affects rapid, efficient, and reliable recovery actions for microgrids after they fail. In this work, we define metrics to evaluate the rebound of microgrid systems and create task networks for assessing the speed of recovery given uncertain and difficult decisions. We develop the Human Performance Impact Recovery Analysis (HPIRA) tool to measure the effects of human factors and human error on power system recovery procedures used at military installations. We measure how long current and microgrid systems can take to recover given best-case and worst-case situations, and estimate the man-hours required for recovery. Overall, this thesis establishes the HPIRA method and answers the following research questions:

  • What are the recovery action(s) needed to restore operation of a microgrid system given intentional attack and natural disaster threat scenarios?
  • How long will microgrid system recovery take given human factors and decision-making?

This work is in collaboration with Dr. Douglas Van Bossuyt of the NPS Systems Engineering Department.


A Computational Framework for Optimization-based Interdependent Infrastructure Analysis and Vulnerability Assessment

Maj Mattias Kuc, German Army | M.S. Thesis in Operations Research (December 2020)


Civilian communities and military installations operate numerous interdependent infrastructure systems to deliver services like power, water, mobility, and communications to people and missions. Diverse infrastructure models are available to analyze system vulnerability, yet a standard architecture for linking pre-existing models for interdependent analysis does not exist. We develop a computational framework to generate combined models that link multiple network flow optimization models together for interdependent analysis. We validate our methods and implementation in the Python programming language with well-studied interdependent energy networks. We further demonstrate the versatility of our methods by developing a new assessment of fictitious energy and transportation networks with models not originally created with interdependencies. Overall, this work develops a standard way to conduct interdependent infrastructure analysis with pre-built models and sets a foundation for future analysis of other interdependencies and systems.