In this work we deal with a generalized variant of the multi-vehicle covering
tour problem (m-CTP). The m-CTP consists of minimizing the total routing
cost and satisfying the entire demand of all customers, without the
restriction of visiting them all, so that each customer not included in any
route is covered. In the m-CTP, only a subset of customers is visited to
fulfill the total demand, but a restriction is put on the length of each route
and the number of vertices that it contains. This paper tackles a
generalized variant of the m-CTP, called the multi-vehicle multi-covering
Tour Problem (mm-CTP), where a vertex must be covered several times instead
of once. We study a particular case of the mm-CTP considering only the
restriction on the number of vertices in each route and relaxing the
constraint on the length (mm-CTP-p). A hybrid metaheuristic is developet by
combining Genetic Algorithm (GA), Variable Neighborhood Descent method
(VND), and a General Variable Neighborhood Search algorithm (GVNS) to solve
the problem. Computational experiments show that our approaches are
competitive with the Evolutionary Local Search (ELS) and Genetic Algorithm
(GA), the methods proposed in the literature.
A new enhanced support vector model based on general variable neighborhood search algorithm for supplier performance evaluation: A case study
