Towards Online Electric Vehicle Scheduling for Mobility-On-Demand Schemes
We study a setting where electric vehicles (EVs) can be hired to drive from pick-up to drop-off stations in a mobility-on-demand (MoD) scheme. Each point in the MoD scheme is equipped with battery charge facility to cope with the EVs’ limited range. Customer-agents announce their trip requests over time, and the goal for the system is to maximize the number of them that are serviced. In this vein, we propose two scheduling algorithms for assigning EVs to agents. The first one is efficient for short term reservations, while the second for both short and long term ones. While evaluating our algorithms in a setting using real data on MoD locations, we observe that the long term algorithm achieves on average 2.08% higher customer satisfaction and 2.87% higher vehicle utilization compared to the short term one for 120 trip requests, but with 17.8% higher execution time. Moreover, we propose a software package that allows for efficient management of a MoD scheme from the side of a company, and easy trip requests for customers.
KeywordsElectric vehicles Mobility on demand Scheduling Demand response Software
- 4.Pavone, M., L. Smith, S., Frazzoli, E., Rus, D.: Load balancing for mobility-on-demand systems. In: Robotics: Science and Systems (2011)Google Scholar
- 5.Smith, S., Pavone, M., Schwager, M., Frazzoli, E., Rus, D.: Rebalancing the rebalancers: optimally routing vehicles and drivers in mobility-on-demand systems. In: 2013 American Control Conference, pp. 2362–2367 (2013)Google Scholar
- 7.Drwal, M., Gerding, E., Stein, S., Hayakawa, K., Kitaoka, H.: Adaptive pricing mechanisms for on-demand mobility. In: Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems, pp. 1017–1025 . International Foundation for Autonomous Agents and Multiagent Systems (2017)Google Scholar