The project addresses the critical challenge of routing and electrifying delivery vehicle fleets to reduce transportation costs and greenhouse gas emissions. By promoting electric vehicles as an environmentally sustainable alternative to internal combustion engine vehicles, the project focuses on the Electric Vehicle Routing Problem (EVRP) and its various extensions. At its scientific core, the project aims to investigate the potential of quantum optimization methods for solving small-sized problems-thereby enhancing the research group’s expertise-and to develop a general metaheuristic method capable of solving multiple EVRP variants. It will explore routing strategies under real-world constraints such as delivery locations, time windows, cargo capacities, and battery limitations. To address the latter, a metaheuristic framework based on Adaptive Large Neighborhood Search (ALNS) will be developed and used to compare the performance of quantum approaches (quantum inspired heuristics or quantum annealing), assessing their applicability to complex EVRP variants. As a foundation for fleet electrification, the project will consider mixed fleets of conventional and electric vehicles, various charging technologies and long-term delivery planning with periodic customer requests. Finally, the project will address the stochastic-dynamic variant of the problem-suitable for modelling robotaxi routing systems-by accounting for dynamic and uncertain environments through the application of machine learning and heuristic methods.