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

Research areas:

• Methodology - Machine learning

Year:

2014

Type of Publication:

Article

Keywords:

Evolutionary Computation, Artificial Neural Networks, Unequal Area Facility Layout Problem, Layout Representation, Basis functions

Authors:

• García-Hernández, L.
• Pérez-Ortiz, María
• Araúzo-Azofra, A.
• Salas-Morera, L.
• Hervás-Martínez, César

Journal:

Neurocomputing

Volume:

135

Pages:

69-78

Month:

July

ISSN:

0925-2312

BibTex:

@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", }

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