Enterprise Risk Analysis and Optimization Model for Vehicle Electrification at Maritime Container Ports

Baker, Robert, Systems Engineering - School of Engineering and Applied Science, University of Virginia
Lambert, James, EN-SIE, University of Virginia

There is urgency for electrifying fleet vehicles as a means to reach net-zero emissions and promote
sustainability, including at maritime container ports. Ports are exploring the incorporation of
electric terminal tractors and supporting infrastructure in an effort to minimize the environmental
effects of their operations while simultaneously improving service performance. The challenges
include planning of investments in infrastructure that will meet charging requirements of these
terminal tractors while maintaining operational efficiencies. This thesis develops a mathematical
optimization and associated enterprise risk analysis to support capacity expansion of electric
vehicle fleets at maritime container ports. The approach characterizes risk as the disruption
of system order. A demonstration of schedule optimization uses linear programming models
for thirty-two combinations of plug-in, wireless, and wireless dynamic charging infrastructure
configurations to determine optimal charger locations. In a robust ensemble model, the
optimization accompanies a comprehensive risk analysis that disrupts importance orders across
seven scenarios: (1) Environmental Change, (2) Policy Revision, (3) Technology Innovation, (4)
Cyber Attack, (5) Market Shift, (6) Electrical Grid Stress, and (7) Workforce Interruption. The
results support the decisions and enterprise risk management for a $1.5 billion strategic plan for
port infrastructure. The plan involves selecting charging station locations, determining charging
schedules, and selecting charger models while considering multiple performance criteria such
as safety, operational efficiency, cost-effectiveness, and reliability. The approach is generally
applicable for a variety of complex systems to mitigate schedule and cost risks while improving
sustainability. The audience of the thesis includes owners and operators of transportation and
energy infrastructures, asset managers, logistics service providers, and others.

MS (Master of Science)
Issued Date: