Autonomous Transfer Hub Networks for Self-Driving Trucks
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Lee, Chungjae
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Abstract
The emergence of autonomous driving technology is expected to fundamentally transform the future of the transportation industry.
In the domain of freight transportation, ATHN, deploying autonomous trucks for the middle mile and traditional trucks on the first and last miles, is perceived as the most likely adoption of this technology.
This thesis develops a scalable and comprehensive framework for optimizing the operations and managing hub utilization within large-scale ATHN systems.
Ultimately, the framework is applied to the entire U.S. freight market, envisioning the future freight transportation and its impact on cost savings and the labor market dynamics.
The first part of the thesis presents a scalable flow-based MIP model designed to select loads for the ATHN and establish delivery schedules.
By exploiting the problem structure, this model achieves optimal ATHN design within hours and facilitates data-driven analysis for large-scale systems.
To demonstrate the MIP model's performance, a case study utilizing real data from a dedicated trucking company is conducted, representing a freight system spanning the entire United States over a one-month horizon.
This study quantifies the potential cost-saving benefits of using autonomous trucks and presents an extensive sensitivity analysis on various factors of the ATHN system.
The second part of the thesis addresses limitations in the MIP model regarding hub operations.
Despite its efficacy, the MIP model does not account for operations within the hub.
Optimizing hub utilization with MIP methodologies requires adjusting the MIP model, resulting in a complex framework where scaling becomes nontrivial.
To overcome this difficulty and optimze hub utilization, a CP model is developed to minimize the hub capacities by shifting the start times in the initial schedule from the MIP model.
The power of combining the strengths of MIP and CP is demonstrated using the same case study data, which shows that the CP model can find optimal schedules efficiently and reduce the necessary hub capacities substantially.
Furthermore, the sensitivity analysis centered on hub operations provides insights for improving the ATHN operations, laying the foundation to achieve the ultimate goal of assessing the economic and labor impact of the ATHN on the entire U.S. freight market.
The final part of the thesis envisions the impact of autonomous trucking on the U.S. freight market by applying the proposed framework to all freight loads.
Two ATHN systems are introduced based on the availability of autonomous trucking.
Due to the absence of a representative dataset for total U.S. freight movement, synthetic load data is generated by processing publicly available freight datasets.
This application constructs an ATHN system capable of accommodating national freight demands while offering insights into labor dynamics.
Additionally, a thorough economic and labor study is conducted using the collaborative market framework as the U.S. freight market gradually adopts the ATHN system.
Analyzing these results is crucial for understanding the landscape of the future freight market, designing labor policies, and effectively implementing the national-scale ATHN system in the years to come.
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Date
2024-12-04
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Text
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Dissertation