A fast metaheuristic approach for the planar multiple obnoxious facility location problem
Abstract
Abstract The multiple obnoxious facility location problem is one of the most studied problems in the literature of the obnoxious facility location problems family. In this work, we propose an alternative algorithmic approach for this problem, based on an efficient metaheuristic procedure over a discretization of the plane based on Voronoi diagrams, helped by a local search able to traverse the continuous space. To this aim, our algorithmic proposal first begins with the implementation of a greedy randomized adaptive search procedure method, whose improvement phase is implemented by a general variable neighborhood search (GVNS) procedure. The GVNS is supported by two local search methods that allow the algorithm to escape the initial discretization and find good solutions in short execution times. The results show that the proposed algorithm achieves the best results regarding the objective function value, deviation, and number of best results in relation to the state of the art. These results are further confirmed by conducting nonparametric statistical tests.
Sergio Salazar
Artificial Intelligence Phd Student
Sergio Salazar graduated in Mathematics and Computer Science from the Rey Juan Carlos University in 2023. He is working here as a predoctoral researcher focusing in Continious Facilities Location Problems.
Abraham Duarte
Full Professor
Abraham Duarte is Full Professor in the Computer Science Department at the Rey Juan Carlos University (Madrid, Spain). He has done extensive research in the interface between computer science, artificial intelligence, and operations research to develop solution methods based on Computational Intelligence (metaheuristics) for practical problems in operations-management areas such as logistics and supply chains, telecommunications, decision-making under uncertainty and optimization of simulated systems.
J. Manuel Colmenar
Full Professor
My research interests are focused on metaheuristics applied to optimization problems. I have worked on different combinatorial optimization problems applying trajectorial algorithms such us GRASP or VNS. Besides, I am very interested in applications of Grammatical Evolution, specifically in model and prediction domain, as alternative to machine learning approaches.