Navegando por Autor "Maia, Marcelo Rodrigues de Holanda"
Agora exibindo 1 - 2 de 2
Resultados por página
Opções de Ordenação
Item Hybrid data mining heuristics for the heterogeneous fleet vehicle routing problem.(2018) Maia, Marcelo Rodrigues de Holanda; Carvalho, Alexandre Plastino de; Penna, Puca Huachi VazThe 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.Item MineReduce : an approach based on data mining for problem size reduction.(2020) Maia, Marcelo Rodrigues de Holanda; Carvalho, Alexandre Plastino de; Penna, Puca Huachi VazHybrid variations of metaheuristics that include data mining strategies have been utilized to solve a variety of combinatorial optimization problems, with superior and encouraging results. Previous hybrid strategies applied mined patterns to guide the construction of initial solutions, leading to more effective exploration of the solution space. Solving a combinatorial optimization problem is usually a hard task because its solution space grows exponentially with its size. Therefore, problem size reduction is also a useful strategy in this context, especially in the case of large-scale problems. In this paper, we build upon these ideas by presenting an approach named MineReduce, which uses mined patterns to perform problem size reduction. We present an application of MineReduce to improve a heuristic for the heterogeneous fleet vehicle routing problem. The results obtained in computational experiments show that this proposed heuristic demonstrates superior performance compared to the original heuristic and other state-of-the-art heuristics, achieving better solution costs with shorter run times.