Optimization of Electric Scooter Rebalancing Tour through Mathematical programming

The pursuit of Sustainable Development Goal 11 (SDG11), striving to create inclusive, resilient, and sustainable urban environments, has become a global priority. One of SDG11’s key objectives is to ensure safe, affordable, accessible, and sustainable transport systems for all. In response to this imperative, the concept of electric scooter (e-scooter) sharing has gained remarkable popularity. This innovative model allows users to access e-scooters on demand, providing a personalized last-mile solution that complements public transportation and has the potential to reduce private car ownership and greenhouse gas emissions. As e-scooters take on an increasingly pivotal role in urban mobility, the efficient management of their distri- bution and charging presents a critical challenge. One of the key problems in this sharing system is to ensure that e-scooter is not over-saturated and under-utilized. This thesis embarks on a comprehensive exploration of the com- plexities surrounding this challenge and proposes solutions to enhance the integration of e-scooter sharing into everyday urban life. The core idea revolves around conducting a night tour operation that allow operator to drop full-charge e-scooter, but also swap the battery of the low charged unit to re-balance the e-scooter distribution. Within this thesis, two mathematical programming formulations are presented in order to plan ahead the route and suggested actions at each station during the night tour. The first model, adapted from previous literature work on bike sharing systems rebalancing, provides a benchmark and introduces readers to the night rebalancing tour problem. The second model represents an original improved version, allowing night tour operators to swap only the battery rather than the whole e-scooter unit during operations. Both models confront the challenge of managing exponential growth in constraints and size of the solution space as the number of stations increases. In response, tailor-made branch-and-cut algorithms are developed to efficiently solve this problem. This scalable framework offers the potential to manage extensive e-scooter fleets and station networks within the city, enabling companies to enhance their operations, foster citizen trust, and establish e-scooter sharing systems as a dependable choice for daily last-mile transportation. This thesis aims to make the topic accessible and easily understandable, inviting broader participation and contributions to research in this vital field.