Abstract

Greenhouse gas–emitting energy sources are a major driver of global warming. As cities pursue sustainability, electrifying public transit has become a key strategy to reduce urban air pollution. Electric buses (EBs) are increasingly adopted as energy-efficient, zero-emission alternatives to diesel fleets. However, EBs have limited range, long charging times, and depend on charger locations, so traditional transit planning models do not apply directly. New optimization models and solution methods are needed to operate EBs efficiently while maintaining a high level of service. In this thesis, we address the emerging optimization challenges of integrating EBs into urban transit systems. First, we conduct a comprehensive literature review in this field to critically assess and classify the existing works and to identify potential areas for future research. The goal is to develop more realistic and applicable models and solution approaches for EB transit systems. Second, we develop a mixed-integer linear programming (MILP) model that jointly optimizes bus timetabling and scheduling to minimize total operational costs while accounting for passenger waiting time and seat availability. Using a normalized weighted-sum approach to balance cost and service quality, we determine optimal headways and demonstrate, in a Montreal university shuttle case study, that the model significantly reduces waiting times and improves seat availability compared to the current schedule. We also address the role of charging infrastructure in EB operations by proposing an integer linear programming model that jointly optimizes bus scheduling and fast-charging station locations, minimizing scheduling, travel, energy, and charger installation costs. Because of the problem’s complexity, we design and test several branch-and-price algorithms with different branching strategies to find the most efficient approach. The model is applied to a real Montreal case study, and a sensitivity analysis is conducted to identify the most cost-effective EB type for the transit authority. The case studies and computational experiments show that the proposed models and solution methods support a smooth transition to a fully electric public transit system. By offering transit agencies practical and effective models, this research advances sustainable urban mobility and contributes to global efforts to address climate change.