Volume 52, Number 3, July–September 2018
|Page(s)||661 - 690|
|Published online||19 September 2018|
Hybrid data mining heuristics for the heterogeneous fleet vehicle routing problem
Instituto de Computação, Universidade Federal Fluminense,
Avenida General Milton Tavares de Souza s/n, Niterói,
2 Instituto Brasileiro de Geografia e Estatística, Rua General Canabarro 706, Rio de Janeiro, RJ, Brazil, 20271–205
3 Instituto de Ciências Exatas e Biológicas, Universidade Federal de Ouro Preto, Campus Universitário Morro do Cruzeiro, Ouro Preto, MG, Brazil, 35400–000
* Corresponding author: firstname.lastname@example.org
Accepted: 5 October 2017
The vehicle routing problem consists of determining a set of routes for a fleet of vehicles to meet the demands of a given set of customers. The development and improvement of techniques for finding better solutions to this optimization problem have attracted considerable interest since such techniques can yield significant savings in transportation costs. The heterogeneous fleet vehicle routing problem is distinguished by the consideration of a heterogeneous fleet of vehicles, which is a very common scenario in real-world applications, rather than a homogeneous one. Hybrid versions of metaheuristics that incorporate data mining techniques have been applied to solve various optimization problems, with promising results. In this paper, we propose hybrid versions of a multi-start heuristic for the heterogeneous fleet vehicle routing problem based on the Iterated Local Search metaheuristic through the incorporation of data mining techniques. The results obtained in computational experiments show that the proposed hybrid heuristics demonstrate superior performance compared with the original heuristic, reaching better average solution costs with shorter run times.
Mathematics Subject Classification: 90B06 / 90C27 / 90C59
Key words: Hybrid metaheuristic / data mining / heterogeneous fleet vehicle routing problem
© EDP Sciences, ROADEF, SMAI 2018
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