An Enhanced R-NSGA-II For Multiple Brands Advertising Campaign Allocation Problem
Fodil Benali, Damien Bodénès, Cyril de Runz, Nicolas Labroche · 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) · 2021
This paper deals with the Campaign Allocation Problem of commercial Ads in TV breaks that we formalize as a multi-stakeholders multiobjective problem with highly competing objectives for different brands and numerous constraints. The problem is NP-hard with a high dimensional objective space and scalability issues in terms of the number of breaks. Moreover, the expected solution should be able to focus on a sub-part of the Pareto front according to decision maker’s (DM) knowledge. To tackle these challenges, we propose to use R-NSGA-II, a Many-Objective Evolutionary Algorithm (MaOEA), combined with a novel gene encoding/decoding process. Experiments show that this approach obtains better results than usual MaOEA (NSGA-II, NSGA-III) according to industrial performance criteria, scales to large instances, and incorporates decision maker’s preferences during the optimization process.