Abstract
Recent advances in the telecommunication industry offer great opportunities to citizens and organizations in a globally connected world, but they also arise a vast number of complex challenges that decision makers must face. Some of these challenges can be modeled as combinatorial optimization problems (COPs). Frequently, these COPs are large-size, NP-hard, and must be solved in “real time,” which makes necessary the use of metaheuristics. The first goal of this paper is to provide a review on how metaheuristics have been used so far to deal with COPs associated with telecommunication systems, detecting the main trends and challenges. Particularly, the analysis focuses on the network design, routing, and allocation problems. In addition, due to the nature of these challenges, the paper discusses how the hybridization of metaheuristics with methodologies such as simulation and machine learning can be employed to extend the capabilities of metaheuristics when solving stochastic and dynamic COPs in the telecommunication industry.
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