@inproceedings {pub5546,
	title = {Improvements in Large Neighborhood Search for the Electric Autonomous Dial-A-Ride Problem},
	author = {Maria Bresich AND Guenther Raidl AND Steffen Limmer},
	year = {2024},
	abstract = {Due to increasing mobility and transportation demands, the interest in shared and on-demand transportation services is growing and leads to high practical relevance of this research area. In the dial-a-ride problem (DARP), a fleet of vehicles has to provide service to users with transportation requests consisting of a pickup and a drop-off location and a time window for either the departure
or arrival, while allowing different customers to share a vehicle. The goal of the standard DARP is to find minimum cost routes serving all requests and satisfying a set of constraints concerning in particular vehicle capacities and user ride times. Diverse variants of the DARP with different objectives and constraints are studied in the literature. In this work, we consider specifically the electric autonomous dial-a-ride problem (E-ADARP) and investigate a large neighborhood search for the E-ADARP that combines and significantly extends concepts from earlier works.},
	publisher = {Springer, LNCS, LNAI, LNBI},
	booktitle = {Eurocast 2024}
}