Date of Award

Summer 8-2022

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computational Modeling & Simulation Engineering

Program/Concentration

Modeling and Simulation

Committee Director

Yuzhong Shen

Committee Member

Rafael Diaz

Committee Member

Jiang Li

Committee Member

Hong Yang

Abstract

Recently the number, variety, and complexity of interconnected systems have been increasing while the resources available to increase resilience of those systems have been decreasing. Therefore, it has become increasingly important to quantify the effects of risks and the resulting disruptions over time as they ripple through networks of systems. This dissertation presents a novel modeling and simulation methodology which quantifies resilience, as impact on performance over time, and risk, as the impact of probabilistic disruptions. This work includes four major contributions over the state-of-the-art which are: (1) cyclic dependencies are captured by separation of performance variables into layers which can have different topologies, (2) temporal dependence is modeled using Bayesian networks to allow for incorporation of evidence-based data over time and produce a dynamic model incorporating risk and resilience behavior over time, (3) a combined approach maps from discrete random variables in the risk network to continuous variables in the system network allowing for the propagation of risk throughout the system, and (4) a decomposable architecture allows various components to be represented at different level of detail and overall system reconfiguration to be explored. Applications are provided in supply chain analysis and port logistics to demonstrate the performance and effectiveness of the methodology.

DOI

10.25777/2pfb-yz37

ISBN

9798352694725

ORCID

0000-0002-5026-4501

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