A GRASP Approach for Solving Large-Scale Electric Bus Scheduling Problems

Electrifying public bus transportation is a critical step in reaching net-zero goals. In this paper, the focus on problem of optimal scheduling an electric (EB) fleet to cover transport timetable. The modelled using mixed integer program (MIP) which charging time EB pertinent battery’s state-of-charge level. To be able solve large instances corresponding real-world applications model, metaheuristic approach investigated. more precise, greedy randomized adaptive search procedure (GRASP) algorithm developed and its performance evaluated against solutions acquired MIP. GRASP used for case studies several systems having various properties sizes. analysis focuses relation between ranges (battery capacity) required rates (in kW) size needed results conducted computational experiments indicate that increase infrastructure investment through high speed chargers can significantly decrease necessary fleets. also show have significant impact than battery sizes EBs.