@article{ENSforUAFLP,
author = "L. Garc{\'i}a-Hern{\'a}ndez and Mar{\'i}a P{\'e}rez-Ortiz and A. Ara{\'u}zo-Azofra and L. Salas-Morera and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "This paper presents an hybrid system for incorporating human expert knowledge into the Unequal Area Facility Layout Problem. For that matter, a subset of facility designs are generated (using a Genetic Algorithm) and then, evaluated by an human expert. The hybrid system is giving a mark, with the aim of substituting the human expert knowledge, avoiding his/her fatigue and burden. The novel proposed approach was tested under a real case study made of 365 facility layout designs from an ovine slaughterhouse. The validation phase of the intelligent model presented is performed by means of a new subset of 181 facility layout designs with the evaluation provided by a different human expert. The results of the experiment, which validate the performance of the proposed approach, are presented and discussed in this study.
",
awards = "JCR(2014): 2.083 Position: 36/124 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
comments = "JCR(2014): 2.083 Position: 36/124 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
doi = "10.1016/j.neucom.2013.01.068",
issn = "0925-2312",
journal = "Neurocomputing",
keywords = "Evolutionary Computation, Artificial Neural Networks, Unequal Area Facility Layout Problem, Layout Representation, Basis functions",
month = "July",
note = "JCR(2014): 2.083 Position: 36/124 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
pages = "69--78",
title = "{A}n {E}volutionary {N}eural {S}ystem for incorporating {H}uman {E}xpert {K}nowledge into the {UA}-{FLP}",
url = "http://dx.doi.org/10.1016/j.neucom.2013.01.068",
volume = "135",
year = "2014",
}