Abstract
The bi-objective Double Floor Corridor Allocation Problem is an operational research problem with the goal of finding the best arrangement of facilities in a layout with two corridors located in two floors, in order to minimize the material handling costs and the corridor length. In this paper, we present a novel approach based on a combination of Path Relinking strategies. To this aim, we propose two greedy algorithms to produce an initial set of non-dominated solutions. In a first stage, we apply an Interior Path Relinking with the aim of improving this set and, in the second stage, apply an Exterior Path Relinking to reach solutions that are unreachable in the first stage. Our extensive experimental analysis shows that our method, after automatic parameter optimization, completely dominates the previous benchmarks, spending shorter computation times. In addition, we provide detailed results for the new instances, including standard metrics for multi-objective problems.
Nicolás Rodríguez Uribe
Phd in Artificial Intelligence
Nicolás Rodríguez Uribe graduated with a degree in Computer Engineering from Universidad Rey Juan Carlos in 2015. Subsequently, he completed a Master’s Degree in Decision Systems Engineering in 2018 and obtained his Doctorate in Artificial Intelligence from the same university in 2022. His main research interests focus on heuristics and metaheuristics, combinatorial optimization, trajectory algorithms, genetic algorithms, and multi-objective problems. He is a member of the high-performance research group in optimization algorithms (GRAFO) at Universidad Rey Juan Carlos. Most of his publications deal with the development of heuristic and metaheuristic procedures to solve complex optimization problems.
Alberto Herrán González
Associate Professor
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.