  As many public transportation systems around the world transition to electric
buses, the planning and operation of fleets can be improved via tailored
decision-support tools. In this work, we study the impact of jointly locating
charging facilities, assigning electric buses to trips, and determining when
and where to charge the buses. We propose a mixed integer linear program that
co-optimizes planning and operational decisions jointly and an iterated local
search heuristic to solve large-scale instances. Herein, we use a concurrent
scheduler algorithm to generate an initial feasible solution, which serves as a
starting point for our iterated local search algorithm. In the sequential case,
we first optimize trip assignments and charging locations. Charging schedules
are then determined after fixing the optimal decisions from the first level.
The joint model, on the other hand, integrates charge scheduling within the
local search procedure. The solution quality of the joint and sequential
iterated local search models are compared for multiple real-world bus transit
networks. Our results demonstrate that joint models can help further improve
operating costs by 14.1% and lower total costs by about 4.1% on average
compared with sequential models. In addition, energy consumption costs and
contracted power capacity costs have been reduced significantly due to our
integrated planning approach.
