Charging demand, Charging station planning, Charging scheduling, Platoon scheduling, Mathematical optimization, Electric truck


Owing to advancements in technology, substantial investments within the automotive industry, and the formulation of supportive state policies, the future landscape of the transportation sector is poised to witness a shift from traditional internal combustion engine vehicles (ICEVs) to electric vehicles (EVs). While EVs have made inroads in the market, they still face significant hurdles in the form of range anxiety and prolonged charging durations, inhibiting their widespread adoption. To tackle these challenges, a comprehensive approach to smart transportation electrification is proposed, emphasizing the pivotal roles of infrastructure development, particularly in the allocation of charging stations, and strategic operational decisions, including charging and platoon scheduling. This dissertation is structured around four essential components. The initial stage entails grasping the intricacies of charging demand, recognized as the foundational step before embarking on any transportation electrification initiative. Subsequently, the allocation of charging stations is addressed, with a specific focus on ride-sourcing vehicles, distinct from private EVs due to issues such as relocation time, waiting time, and dynamic pricing that affects spatiotemporal value of time (VOT) costs. This approach, which considers VOT costs, is essential in avoiding biased results in the planning of charging infrastructure for electrified ride-sourcing services. The third chapter centers on the optimization of charging and platoon scheduling, particularly within the context of long-haul freight vehicles. The objective here is to harness the flexibility of charging schedules to facilitate vehicle platooning, thereby reducing the demand for charging, and, consequently, energy consumption. This chapter involves the development of a mixed-integer programming model and explores various techniques, such as hyperparameter tuning and hybrid meta-heuristic methods, to optimize the model for large-scale applications. Lastly, the fourth chapter takes on the challenge of addressing uncertainty in scheduling problems. This is achieved by formulating a two-stage stochastic model and applying it within a hypothetical numerical example, providing a framework for optimizing charging station (CS) planning while accounting for uncertain operational parameters.

Completion Date




Committee Chair

Guo, Zhaomiao


Doctor of Philosophy (Ph.D.)


College of Engineering and Computer Science


Civil, Environmental, and Construction Engineering








Release Date

December 2023

Length of Campus-only Access


Access Status

Doctoral Dissertation (Open Access)

Campus Location

Orlando (Main) Campus