Hybrid data mining heuristics for the heterogeneous fleet vehicle routing problem

Abstract

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.