A Path Relinking-Based Approach for the Bi-Objective Double Floor Corridor Allocation Problem

Abstract

The Bi-Objective Double Floor Corridor Allocation Problem is one of the most recent incorporation to the family of Facility Layout Problems. This problem, which has been a challenge for exact and metaheuristic approaches, involves optimizing the layout of the given facilities to minimize material handling cost and the length of the corridor considering more than one floor. This paper introduces a new approach based on the combination of two greedy methods and a path relinking implementation to tackle this problem. The experimental results show the superiority of our proposal in relation to the current state-of-the-art under different multi-objective metrics.

Publication
Advances in Artificial Intelligence
Nicolás Rodríguez Uribe
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
Alberto Herrán González
Associate Professor
J. Manuel Colmenar
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.