Research

This research examines a resource planning for shared mobility based evacuation problem, optimizing the location and number of volunteer drivers to recruit to give rides to carless individuals in case of a short-notice disaster, under uncertainty in the spatiotemporal distribution of demand for services. We utilize a two-stage stochastic optimization model, comparing the efficiency and reliability of an expectation-based objective and a chance-constrained formulation. We conduct computational tests using five regional subnetworks of varying sizes and population density, based on Charleston County, South Carolina, which experienced a mandatory evacuation for Hurricane Florence in 2018.

Paper Accepted. Moug, K., Jia, H., Shen, S. “A Shared Mobility Based Framework for Evacuation Planning and Operations under Demand Uncertainty,” to appear in IISE Transactions. (link)

For isolated, densely populated facilities, such as prisons and nursing homes, it is difficult to enact social distancing measures when catastrophic COVID-19 outbreaks occur. In such facilities, de-densification enhances social distancing, yet has inherent costs, e.g., the potential for recidivism in crime for prisons, or the financial cost of incentives for residents to break contracts in nursing homes. We examine the problem of de-densification in isolated facilities with competing factors, using a multi-criteria Markov Decision Process.

Working Paper. Moug, K., Shen, S. “Multi-Criteria Markov Decision Processes for De-Densification of Isolated Facilities During a Pandemic,” December 2020. In preparation for submission.

In this thesis, we explored power domination in vertex sum graphs. Any graph with a cut vertex can be seen as a vertex sum graph. We related power domination in the vertex sum supergraph to power domination in its subgraphs, with a focus on a particular example of a vertex sum graph, a unicyclic graph.