You are cordially invited to attend my thesis defense.
Thesis Title: Autonomous Transfer Hub Networks for Self-Driving Trucks
Thesis Committee:
Dr. Pascal Van Hentenryck (advisor), School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Alan Erera, School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Chelsea White, School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Reem Khir, Edwardson School of Industrial Engineering, Purdue University
Dr. Wim Nuijten, Mathematics and Computer Science, Eindhoven University of Technology
Date and Time: Tuesday, November 26th 2024, 12:00 - 14:00 (EST)
In-Person Location: Coda C1015 Vinings
teams.microsoft.com
Meeting ID: 228 791 327 094
Passcode: jzFrPk
Abstract: The emergence of autonomous driving technology is expected to fundamentally transform the future of the transportation industry. In freight transportation, Autonomous Transfer Hub Network (ATHN) where autonomous trucks handle middle-mile routes and traditional trucks manage the first and last miles, are seen as the most viable application of this technology. This thesis develops a scalable framework to optimize operations and hub utilization for large-scale ATHN systems. The first part introduces a scalable flow-based Mixed Integer Programming (MIP) model designed to select loads and schedule deliveries efficiently in large-scale ATHN systems. A case study using real load data from a U.S. dedicated trucking company demonstrates significant cost savings and provides insights through extensive sensitivity analysis. The second part expands the ATHN framework by incorporating hub operations and labor utilization. A Constraint Programming (CP) model is introduced to complement the MIP, minimizing hub capacity needs by adjusting start times in the MIP schedule. This combined approach is validated using real case study data, confirming reduced hub capacity requirements and improved utilization. The final part broadens the scope of ATHN analysis by introducing a framework that combines data processing with MIP and CP models to quantitatively evaluate their national economic and labor impacts, filling a gap in past research that has largely relied on qualitative evaluations. This approach is illustrated through a case study based on all freight loads across the United States, using data from the Commodity Flow Survey (CFS) and Freight Analysis Framework (FAF5). The case study provides insights into the labor market changes and broader economic impacts of automation at a national level.
Best regards,
Chung Jae
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