An Evolutionary Neural System for incorporating Human Expert Knowledge into the UA-FLP

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Research areas:
Year:
2014
Type of Publication:
Article
Keywords:
Evolutionary Computation, Artificial Neural Networks, Unequal Area Facility Layout Problem, Layout Representation, Basis functions
Authors:
Journal:
Neurocomputing
Volume:
135
Pages:
69-78
Month:
July
ISSN:
0925-2312
BibTex:
Note:
JCR(2014): 2.083 Position: 36/124 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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.
Comments:
JCR(2014): 2.083 Position: 36/124 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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