Abstract
This paper presents a Multi-Objective Optimization (MOO) approach for Out-of-Home (OOH) advertising campaign billboard selection. In particular, it exploits a large variety of features from different sources, such as Geographic Information Systems (GIS) and demographics data, for the construction of billboard profiles that take into account all factors that affect the attractiveness of each billboard both in general and for different types of customers. These profiles are utilized by a Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) hybridized with two problem specific techniques to provide a set of non-dominated solutions, each corresponding to a different allocation of billboards to a given campaign. The experimental results enable exploration of the trade-offs between multiple conflicting objectives (e.g., cost vs. coverage) as well as demonstrate that the two problem specific techniques have improved the conventional MOEA/D performance with respect to both convergence and diversity.
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Nader, N., Alexandrou, R., Iasonos, I., Pamboris, A., Papadopoulos, H., Konstantinidis, A. (2022). A Multi-Objective Optimization Algorithm for Out-of-Home Advertising. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 647. Springer, Cham. https://doi.org/10.1007/978-3-031-08337-2_23
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DOI: https://doi.org/10.1007/978-3-031-08337-2_23
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