A New Artificial Immune System Algorithm for Multiobjective Fuzzy Flow Shop Problems

Cengiz Kahraman; Orhan Engin; Mustafa Kerim Yılmaz

doi:10.2991/ijcis.2009.2.3.5

A New Artificial Immune System Algorithm for Multiobjective Fuzzy Flow Shop Problems

Cengiz Kahraman^*, Orhan Engin, Mustafa Kerim Yılmaz

^*Bu çalışma için yazışmadan sorumlu yazar

Endüstri Mühendisliği Bölümü

Selcuk University

Araştırma sonucu: Dergiye katkı › Makale › bilirkişi

1 Atıf (Scopus)

Özet

In this paper a new artificial immune system (AIS) algorithm is proposed to solve multi objective fuzzy flow shop scheduling problems. A new mutation operator is also described for this AIS. Fuzzy sets are used to model processing times and due dates. The objectives are to minimize the average tardiness and the number of tardy jobs. The developed new AIS algorithm is tested on real world data collected at an engine cylinder liner manufacturing process. The feasibility and effectiveness of the proposed AIS is demonstrated by comparing it with genetic algorithms. Computational results demonstrate that the proposed AIS algorithm is more effective meta-heuristic for multi objective flow shop scheduling problems with fuzzy processing time and due date.

Orijinal dil	İngilizce
Sayfa (başlangıç-bitiş)	236-247
Sayfa sayısı	12
Dergi	International Journal of Computational Intelligence Systems
Hacim	2
Basın numarası	3
DOI'lar	https://doi.org/10.2991/ijcis.2009.2.3.5
Yayın durumu	Yayınlandı - Eki 2009

Bibliyografik not

Publisher Copyright:
© 2009, the authors.

Belgeye Erişim

10.2991/ijcis.2009.2.3.5

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

@article{f186a99b8263445084148579b3d646f0,

title = "A New Artificial Immune System Algorithm for Multiobjective Fuzzy Flow Shop Problems",

abstract = "In this paper a new artificial immune system (AIS) algorithm is proposed to solve multi objective fuzzy flow shop scheduling problems. A new mutation operator is also described for this AIS. Fuzzy sets are used to model processing times and due dates. The objectives are to minimize the average tardiness and the number of tardy jobs. The developed new AIS algorithm is tested on real world data collected at an engine cylinder liner manufacturing process. The feasibility and effectiveness of the proposed AIS is demonstrated by comparing it with genetic algorithms. Computational results demonstrate that the proposed AIS algorithm is more effective meta-heuristic for multi objective flow shop scheduling problems with fuzzy processing time and due date.",

keywords = "engine cylinder liner manufacturing process, Fuzzy flow shop, multi objective, new artificial immune system",

author = "Cengiz Kahraman and Orhan Engin and Yılmaz, {Mustafa Kerim}",

note = "Publisher Copyright: {\textcopyright} 2009, the authors.",

year = "2009",

month = oct,

doi = "10.2991/ijcis.2009.2.3.5",

language = "English",

volume = "2",

pages = "236--247",

journal = "International Journal of Computational Intelligence Systems",

issn = "1875-6891",

publisher = "Springer International Publishing AG",

number = "3",

}

TY - JOUR

T1 - A New Artificial Immune System Algorithm for Multiobjective Fuzzy Flow Shop Problems

AU - Kahraman, Cengiz

AU - Engin, Orhan

AU - Yılmaz, Mustafa Kerim

PY - 2009/10

Y1 - 2009/10

N2 - In this paper a new artificial immune system (AIS) algorithm is proposed to solve multi objective fuzzy flow shop scheduling problems. A new mutation operator is also described for this AIS. Fuzzy sets are used to model processing times and due dates. The objectives are to minimize the average tardiness and the number of tardy jobs. The developed new AIS algorithm is tested on real world data collected at an engine cylinder liner manufacturing process. The feasibility and effectiveness of the proposed AIS is demonstrated by comparing it with genetic algorithms. Computational results demonstrate that the proposed AIS algorithm is more effective meta-heuristic for multi objective flow shop scheduling problems with fuzzy processing time and due date.

AB - In this paper a new artificial immune system (AIS) algorithm is proposed to solve multi objective fuzzy flow shop scheduling problems. A new mutation operator is also described for this AIS. Fuzzy sets are used to model processing times and due dates. The objectives are to minimize the average tardiness and the number of tardy jobs. The developed new AIS algorithm is tested on real world data collected at an engine cylinder liner manufacturing process. The feasibility and effectiveness of the proposed AIS is demonstrated by comparing it with genetic algorithms. Computational results demonstrate that the proposed AIS algorithm is more effective meta-heuristic for multi objective flow shop scheduling problems with fuzzy processing time and due date.

KW - engine cylinder liner manufacturing process

KW - Fuzzy flow shop

KW - multi objective

KW - new artificial immune system

UR - http://www.scopus.com/inward/record.url?scp=85181166777&partnerID=8YFLogxK

U2 - 10.2991/ijcis.2009.2.3.5

DO - 10.2991/ijcis.2009.2.3.5

M3 - Article

AN - SCOPUS:85181166777

SN - 1875-6891

VL - 2

SP - 236

EP - 247

JO - International Journal of Computational Intelligence Systems

JF - International Journal of Computational Intelligence Systems

IS - 3

ER -

A New Artificial Immune System Algorithm for Multiobjective Fuzzy Flow Shop Problems

Özet

Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap