Abstract Manager

As global attention intensifies on environmental sustainability, the shift from diesel to electric buses in public transportation systems is gaining crucial importance. This transition extends beyond mere replacement of diesel buses; it encompasses the challenge of infrastructure planning and its optimal utilization. Our paper introduces a mixed-integer optimization model enhanced by a heuristic algorithm, aimed at creating effective charging schedules for electric buses, an essential step towards a greener transit system. 
The core of our approach lies in tackling the challenges posed by multiple buses competing for limited charging stations. Directly solving the mixed-integer problem often results in excessive computational times and impractical solutions. To address this, we propose a heuristic algorithm that provides efficient and workable scheduling solutions. This model considers various factors, including charging rate, required safety charge level, and the limited duration a bus can spend connected to a charger. Importantly, the algorithm incorporates penalties for overly utilized chargers, aiming to minimize these penalties to enhance overall bus scheduling. 
A significant contribution of our research is the development of an interactive tool that utilizes bus schedules to propose optimal charging strategies. This tool offers transit planners the flexibility to adjust key parameters like battery capacity and minimum charge levels, charging rate, changing capacity of each charging station, allowing for a tailored approach to each unique transit scenario.  

Our case study, employing a GTFS dataset and energy consumption metrics from WSP's Bolt Platform, exemplifies the practical application of our heuristic model. By optimizing bus schedules and developing an interactive tool, we demonstrate a scalable and adaptable solution for electric bus charging optimization.  

Our approach offers a robust solution to the complexities of scheduling electric bus charging. It encourages transit agencies to adopt data-driven methods for improving the efficiency and environmental friendliness of their operations. The tool developed through this research represents a significant step towards enhancing the resilience of transportation systems in the face of climate challenges. By optimizing electric bus operations, we contribute to the broader goal of creating more sustainable and resilient transportation infrastructure.