An artificial immune system based algorithm to solve unequal area facility layout problem

Berna Haktanirlar Ulutas, Sadan Kulturel-Konak

Research output: Contribution to journalArticle

54 Citations (Scopus)

Abstract

This study introduces an artificial immune system (AIS) based algorithm to solve the unequal area facility layout problem (FLP) with flexible bay structure (FBS). The proposed clonal selection algorithm (CSA) has a new encoding and a novel procedure to cope with dummy departments that are introduced to fill the empty space in the facility area. The algorithm showed consistent performance for the 25 test problem cases studied. The problems with 100 and 125 were studied with FBS first time in the literature. CSA provided four new best FBS solutions and reached to sixteen best-so-far FBS solutions. Further, the two very large size test problems were solved first time using FBS representation, and results significantly improved the previous best known solutions. The overall results state that CSA with FBS representation was successful in 95.65% of the test problems when compared with the best-so-far FBS results and 90.90% compared with the best known solutions that have not used FBS representation.

Original languageEnglish (US)
Pages (from-to)5384-5395
Number of pages12
JournalExpert Systems With Applications
Volume39
Issue number5
DOIs
StatePublished - Apr 1 2012

Fingerprint

Flexible structures
Immune system

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

@article{0fcd2ecc03e34bf68ee5f7056b7e1952,
title = "An artificial immune system based algorithm to solve unequal area facility layout problem",
abstract = "This study introduces an artificial immune system (AIS) based algorithm to solve the unequal area facility layout problem (FLP) with flexible bay structure (FBS). The proposed clonal selection algorithm (CSA) has a new encoding and a novel procedure to cope with dummy departments that are introduced to fill the empty space in the facility area. The algorithm showed consistent performance for the 25 test problem cases studied. The problems with 100 and 125 were studied with FBS first time in the literature. CSA provided four new best FBS solutions and reached to sixteen best-so-far FBS solutions. Further, the two very large size test problems were solved first time using FBS representation, and results significantly improved the previous best known solutions. The overall results state that CSA with FBS representation was successful in 95.65{\%} of the test problems when compared with the best-so-far FBS results and 90.90{\%} compared with the best known solutions that have not used FBS representation.",
author = "{Haktanirlar Ulutas}, Berna and Sadan Kulturel-Konak",
year = "2012",
month = "4",
day = "1",
doi = "10.1016/j.eswa.2011.11.046",
language = "English (US)",
volume = "39",
pages = "5384--5395",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Limited",
number = "5",

}

An artificial immune system based algorithm to solve unequal area facility layout problem. / Haktanirlar Ulutas, Berna; Kulturel-Konak, Sadan.

In: Expert Systems With Applications, Vol. 39, No. 5, 01.04.2012, p. 5384-5395.

Research output: Contribution to journalArticle

TY - JOUR

T1 - An artificial immune system based algorithm to solve unequal area facility layout problem

AU - Haktanirlar Ulutas, Berna

AU - Kulturel-Konak, Sadan

PY - 2012/4/1

Y1 - 2012/4/1

N2 - This study introduces an artificial immune system (AIS) based algorithm to solve the unequal area facility layout problem (FLP) with flexible bay structure (FBS). The proposed clonal selection algorithm (CSA) has a new encoding and a novel procedure to cope with dummy departments that are introduced to fill the empty space in the facility area. The algorithm showed consistent performance for the 25 test problem cases studied. The problems with 100 and 125 were studied with FBS first time in the literature. CSA provided four new best FBS solutions and reached to sixteen best-so-far FBS solutions. Further, the two very large size test problems were solved first time using FBS representation, and results significantly improved the previous best known solutions. The overall results state that CSA with FBS representation was successful in 95.65% of the test problems when compared with the best-so-far FBS results and 90.90% compared with the best known solutions that have not used FBS representation.

AB - This study introduces an artificial immune system (AIS) based algorithm to solve the unequal area facility layout problem (FLP) with flexible bay structure (FBS). The proposed clonal selection algorithm (CSA) has a new encoding and a novel procedure to cope with dummy departments that are introduced to fill the empty space in the facility area. The algorithm showed consistent performance for the 25 test problem cases studied. The problems with 100 and 125 were studied with FBS first time in the literature. CSA provided four new best FBS solutions and reached to sixteen best-so-far FBS solutions. Further, the two very large size test problems were solved first time using FBS representation, and results significantly improved the previous best known solutions. The overall results state that CSA with FBS representation was successful in 95.65% of the test problems when compared with the best-so-far FBS results and 90.90% compared with the best known solutions that have not used FBS representation.

UR - http://www.scopus.com/inward/record.url?scp=84855866128&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84855866128&partnerID=8YFLogxK

U2 - 10.1016/j.eswa.2011.11.046

DO - 10.1016/j.eswa.2011.11.046

M3 - Article

AN - SCOPUS:84855866128

VL - 39

SP - 5384

EP - 5395

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 5

ER -