An efficient Variable Neighborhood Search for the Space-Free Multi-Row Facility Layout problem
Abstract
The Space-Free Multi-Row Facility Layout problem (SF-MRFLP) seeks for a non-overlapping layout of departments (facilities) on a given number of rows satisfying the following constraints: no space is allowed between two adjacent facilities and the left-most department of the arrangement must have zero abscissa. The objective is to minimize the total communication cost among facilities. In this paper, a Variable Neighborhood Search (VNS) algorithm is proposed to solve this -Hard problem. It has practical applications in the context of the arrangement of rooms in buildings, semiconductor wafer fabrication, or flexible manufacturing systems. A thorough set of preliminary experiments is conducted to evaluate the influence of the proposed strategies and to tune the corresponding search parameters. The best variant of our algorithm is tested over a large set of 528 instances previously used in the related literature. Experimental results show that the proposed algorithm improves the state-of-the-art methods, reaching all the optimal values or, alternatively, the best-known values (if the optimum is unknown) but in considerably shorter computing times. These results are further confirmed by conducting a Bayesian statistical analysis.
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.
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.