Date of Award

Fall 2025

Access Type

Dissertation - Open Access

Degree Name

Doctor of Philosophy in Aviation

Department

College of Aviation

Committee Chair

Dothang Truong

Committee Chair Email

TRUONGD@erau.edu

First Committee Member

Jane Pan

First Committee Member Email

PANJ@erau.edu

Second Committee Member

Ahmed Abdelghany

Second Committee Member Email

abdel776@erau.edu

Third Committee Member

Fariba Alamdari

Third Committee Member Email

fariba@faribaalamdari.com

College Dean

Alan J. Stolzer

Abstract

This dissertation focuses on developing and validating an empirical set of simulation models for an air cargo network under large-scale disruption to test recovery strategies that improve resilience. Understanding and quantifying network resilience and recovery strategies is critical for ensuring sustainable operations. These operations become essential during disruptive events as air cargo is used to transport time-sensitive and critical goods. The collective research steps result in a Monte Carlo simulation framework to evaluate air cargo network resilience and identify strategies that enhance performance during disruptions.

The modeled system is a hub-and-spoke network centered on Memphis (MEM) with 23 domestic airports representing the geographic and operational diversity of the U.S. air cargo system. The key model inputs are scheduled flights and airport capacities paired with Gamma distributions (capacity variability) and Poisson processes (arriving and departing flight disruption). The distributions were calibrated to empirical data so that network behavior drives simulation outcomes. The resulting key performance metrics are realized flights, cargo throughput, and normalized performance at airport and network levels. For comparative analysis, the cargo throughput difference suggests a more practical metric than an airport or network resilience score. Of the three, the most comprehensive metric is realized flights, and it is the basis for performance and cargo throughput calculations. The models make theoretical contributions to the body of knowledge by developing a model measuring air cargo network resilience performance, offering a validated tool for strategy testing and evaluation, and providing methodological rigor for resilience analysis under uncertainty. This work also provides a method for translating the stochastic dynamics of air cargo networks into a usable model.

Three recovery strategies were selected and incorporated into the models to test their effectiveness against disruption. The strategies focused on the resource layer of the network: substitution, scalability, and repurposing. Substitution used varying levels of aircraft payload capacity, scalability increased realized flights, and repurposing incorporated the use of commercial aircraft for cargo purposes. The results showed that all three can provide varying levels of improvement for cargo throughput if the strategies are incorporated during lower levels of disruption (e.g., 25 to 50% disruption). Of the three recovery strategies, scaling the number of arriving and departing flights yields the greatest improvement for an air cargo network under disruption. The second was substituting aircraft for a larger payload capacity, and the third was repurposing commercial aircraft. Practically, these findings demonstrated that a structured approach to capacity and contingency planning can influence network resilience. These findings can also be translated into actionable playbooks for capacity management, fleet allocation, and surge planning. Ultimately, this method provides a means for operators to safeguard operational continuity by sustaining throughput and service during disruptions and accelerating time to recovery.

Additional Files
GS9_Acceptance_Hashemi.pdf (379 kB)
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