A Basic Variable Neighborhood Search Approach for the Bi-objective Multi-row Equal Facility Layout Problem
Abstract
The Bi-Objective Multi-Row Facility Layout Problem is a problem belonging to the family of Facility Layout Problems. This problem is challenging for exact and metaheuristics approaches. We use the Pareto front approach instead of the weight approach by means of a non-dominated solution set which we update in order to keep only the non-dominated solutions. To tackle this problem, we propose a Basic VNS algorithm based on a constructive method that generates random solutions, a mono-objective local search that relies on an interchange move, and a shake method that applies insert moves. In this regard, we also explain how to adapt the mono-objective schema of the BVNS for a multi-objective one. Then, we compare our results with the state of the art and propose future work.
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.