Optimization of real-world outdoor campaign allocations

Abstract

In this paper, we investigate the outdoor campaign allocation problem (OCAP), which asks for the distributionof campaign items to billboards considering a number of constraints. In particular, for a metropolitan city witha large number of billboards, the problem becomes challenging. We propose a genetic algorithm-based method toallocate campaign items effectively, and we compare our results with those of nonlinear integer programming and greedyapproaches. Real-world data sets are collected with the given constraints of the price class ratios of billboards located inİstanbul and the budgets of the given campaigns. The methods are evaluated in terms of the efficiency of the constructedplans and the construction time of the planning. The results reveal that the genetic algorithm-based approach gives closeto optimal results in the shortest scheduling time for the OCAP, and it scales linearly with the increasing data sizes.