@article {pub4586pub4750,
	title = {A General Cooperative Optimization Approach for Distributing Service Points in Mobility Applications},
	author = {Thomas Jatschka AND Guenther Raidl AND Tobias Rodemann},
	year = {2021},
	month = {August},
	abstract = {We present a cooperative optimization approach (COA) for distributing service points for mobility applications, which generalizes and refines a previously proposed method. COA is an
iterative framework for optimizing service point locations, combining an optimization component with user interaction on a large scale and a machine learning component that learns user needs and provides the objective function for the optimization. The previously proposed COA was designed for mobility applications in which single service points are sufficient for satisfying individual 
user demands. Here, we also consider applications in which the satisfaction of demands relies on the existence of two or more suitably located service stations, such as in the case of bike/car 
sharing systems. In an experimental evaluation we give a thorough analysis of the performance of COA with special consideration of the number of user interactions required to find near optimal 
solutions. The algorithm is tested on artificial instances as well as instances derived from real-world taxi data from Manhattan. Results show that the approach can effectively solve instances
with hundreds of potential service point locations and thousands of users while keeping the user interactions reasonably low.},
	publisher = {MDPI},
	journal = {Algorithms},
	series = {2021 Selected Papers from Algorithms Editorial Board Members},
	address = {https://www.mdpi.com/1999-4893/14/8/232}
}