Talks and Poster Presentations (with Proceedings-Entry):

T. Jatschka, T. Rodemann, G. Raidl:
"A Cooperative Optimization Approach for Distributing Service Points in Mobility Applications";
Talk: Evolutionary Computation in Combinatorial Optimization (EvoCOP), Leipzig, Germany; 2019-04-24 - 2019-04-26; in: "Evolutionary Computation in Combinatorial Optimization 2019", Lecture Notes in Computer Science, 11452 (2019), ISBN: 978-3-030-16711-0; 1 - 16.

English abstract:
We investigate a variant of the facility location problem concerning
the optimal distribution of service points with incomplete information
within a certain geographical area. The application scenario is
generic in principle, but we have the setup of charging stations for electric
vehicles or rental stations for bicycles or cars in mind. When planning
such systems, estimating under which conditions which customer
demand can be ful lled is fundamental in order to evaluate and optimize
possible solutions. In this paper we present a cooperative optimization
approach for distributing service points that incorporates potential customers
not only in the data acquisition but also during the optimization
process. A surrogate objective function is used to evaluate intermediate
solutions during the optimization. The quality of this surrogate objective
function is iteratively improved by learning from the feedback of
potential users given to candidate solutions. For the actual optimization
we consider a population based iterated greedy algorithm. Experiments
on arti cial benchmark scenarios with idealized simulated user behavior
show the learning capabilities of the surrogate objective function and the
e ectiveness of the optimization.

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.