Routing Scheduling for Valet-Charging Service With Self-Operating and Third-Party Charging Stations
Many electric vehicle (EV) companies have begun providing energy replenishment services to EV owners. Valet-charging is one such service where valet-staff are dispatched to substitute for EV owners in charging their EVs. This paper considers a routing scheduling problem for the service. We explore the charging options available at both the service provider’s self-operating charging stations and third-party charging stations. We formulate the problem via a robust optimization framework to capture the uncertain waiting time at the third-party stations. A Partial-Branch-and-Price algorithm (PB&B) is designed to solve the problem to optimal efficiently for large scales. Specifically, we innovatively proposed acceleration methods to speed up our extended robust labelling algorithm within the PB&B. We conduct a case study to show the effect of uncertainty in detail and provide investment suggestions for the firm’s investment on whether to contract a third-party charging station. Note to Practitioners—The inconvenience of charging has received significant attention for EVs, and energy replenishment services offer an effective approach beyond infrastructure expansion. Valet-charging, first introduced by NIO Inc., followed by FAW-Volkswagen and NETA AUTO, represents a notable attempt in this direction. Valet-staff routing scheduling needs to consider station assignment decisions, which introduces another potential issue: the number of charging stations owned by valet-charging service providers is insufficient. Including third-party charging stations with uncertain waiting times complicates this issue further. We believe our work develops an effective tool for valet-charging service providers to handle routing scheduling with both types of stations and analyze the investment value on charging stations.
Authors
- Na Li (ORCID: https://orcid.org/0000-0002-2129-0241)
- Tianyi Zhao (ORCID: https://orcid.org/0009-0008-6511-4136)
Institutions
- Shanghai Jiao Tong University (CN)
Publication Details
- Journal
- IEEE Transactions on Automation Science and Engineering
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/tase.2025.3551799
- Primary Topic
- Electric Vehicles and Infrastructure
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China