Abstract
We study a setting where electric vehicles (EVs) can be hired to drive from pick-up to drop-off points in a mobility-on-demand (MoD) scheme. Each point in the MoD scheme is equipped with a battery swap facility that helps cope with the EVs' limited range. The goal of the system is to maximise the number of customers that are serviced. Thus, we first model and solve this problem optimally using Mixed-Integer Programming (MIP) techniques and show that the solution scales up to medium sized problems. Given this, we develop a greedy heuristic algorithm that is shown to generate near-optimal solutions and can scale to thousands of consumer requests and EVs. Both algorithms are evaluated in a setting using data of real locations of shared vehicle pick-up and drop-off stations and the greedy algorithm is shown to be on average 90% of the optimal in terms of average task completion.
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