A biased random-key genetic algorithm for the tree of hubs location problem

Hubs are facilities used to treat and dispatch resources in a transportation network. The main idea of Hub Location Problems (HLP) is to locate a number of hubs in a network and route resources from origins to destinations such that the total cost of attending all demands is minimized. In this study, we investigate a particular HLP, called the Tree of Hubs Location Problem in which hubs are connected by means of a tree and the overall network infrastructure relies on a spanning tree. This problem is particularly interesting when the total cost of building the hub backbone is high. In this paper, we propose a biased random key genetic algorithm for solving the tree of hubs location problem. Computational results show that the proposed heuristic is a robust and effective method to tackle this problem. The method was able to improve some best known solutions from the benchmark instances used in the experiments.