An Efficient Genetic Algorithm for the Design of Hub-and-Spoke Networks

• Published in: IEEE communications letters Vol. 17; no. 4; pp. 793 - 796
• Main Authors: Gomes, Bruno N., Martins, Alexandre X., de Camargo, Ricardo S., Ramirez, Jaime A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-04-2013; Institute of Electrical and Electronics Engineers
• Subjects: Algorithmics. Computability. Computer arithmetics; Applied sciences; combinatorial optimization; Computer science; control theory; systems; Encoding; Europe; Exact sciences and technology; Flows in networks. Combinatorial problems; Genetic algorithms; Hub-and-spoke network design; Logistics; Measurement; Operational research and scientific management; Operational research. Management science; Resource management; Sociology; Statistics; Theoretical computing; Hub-and-spoke network design; Local search; Network hub; Evolutionary algorithm; Aeronautics; Interconnected power system; genetic algorithms; Randomized algorithm; Combinatorial optimization; Search algorithm; Computation time; Genetic algorithm; Greedy algorithm; Logistics
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2013.021913.122677

We propose an efficient genetic algorithm (GA) for the design of hub-and-spoke networks with single allocation. The creation of the initial population is based on the greedy randomized search procedure, which provides high quality individuals. Furthermore, new crossover and mutation operators were implemented in order to improve the solution over the evolutionary process. Additionally, a local search procedure is applied in the best individuals. The adapted GA is tested in the Australian Post (AP) and Civil Aeronautics Board (CAB) data sets and clearly outperforms four other evolutionary algorithms of the literature, both in solution quality and CPU time.