An Efficient Algorithm for the T-Row Facility Layout Problem

Abstract

Facility layout problems represent a challenge to the operations research community. These problems are closely related to real-world scenarios in industry and society, such as the design of production factories or the layout of facilities in medical centers, to name a few. These scenarios have been studied from the theoretical point of view as different optimization problems. Among them, we have studied the T-Row Facility Layout Problem, which considers a layout formed by two orthogonal rows where facilities have to be placed minimizing the material handling cost. To efficiently solve this problem we propose a Variable Neighborhood Search algorithm which is able to reach all the optimal solutions reported in the literature spending a fraction of the execution time of the previous algorithm.

Publication
Lecture Notes in Computer Science
Raúl Martín Santamaría
Raúl Martín Santamaría
Phd in Artificial Intelligence

My research interests include…

Alberto Herrán González
Alberto Herrán González
Associate Professor
Abraham Duarte
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
J. Manuel Colmenar
Associate 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.