Hybrid data mining heuristics for the heterogeneous fleet vehicle routing problem.
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Data
2018
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Resumo
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.
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Hybrid metaheuristic
Citação
MAIA, M. R. de H.; CARVALHO, A. P. de; PENNA, P. H. V. Hybrid data mining heuristics for the heterogeneous fleet vehicle routing problem. RAIRO Operations Research, v. 52, n. 3, p. 661–690, jul./set. 2018. Disponível em: <https://www.rairo-ro.org/articles/ro/abs/2018/03/ro160323/ro160323.html>. Acesso em: 19 mar. 2019.