Journals / Gazi Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2002 / Cilt: 15 - Sayı: 2

The genetic algorithms and theirs using in the facilities layout problem: Theory and methodology (Part II)

Genetik algoritmalar ve tesis düzenlemesi probleminde kullanımı: Teori ve metodoloji (Kısım 2)

Pages
615–631
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The aim of this study is to make a systematic definition of the data is needed in the GA assisted solution of FLP and to present both a méthodologie frame and the bibliography in order to assist the studies that will be made on this subject.

📌 3 Key Takeaways

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Objective & Hypothesis:First reseachers of the Facility Layout Problem (FLP) focused on Quadratic Assignment Problem (QAP) intensely.
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Key Findings & Data:But, due to NP-hard structure of QAP, Genetic Algorithms (GAs) can be used in FLP successfully as you start from more than one points in search solution space and also there is no need to use a geometric limit in the model.
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Conclusion & Impact:The aim of this study is to make a systematic definition of the data is needed in the GA assisted solution of FLP and to present both a méthodologie frame and the bibliography in order to assist the studies that will be made on this subject.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

First reseachers of the Facility Layout Problem (FLP) focused on Quadratic Assignment Problem (QAP) intensely. But, due to NP-hard structure of QAP, Genetic Algorithms (GAs) can be used in FLP successfully as you start from more than one points in search solution space and also there is no need to use a geometric limit in the model. In this study, first the needed data and approaches for the GA assisted solution of FLP is searched systematically, then a methodological frame on subject of how to use GAs in FLP is tried to be presented. Again, in order to improve the solution time of QAP "partial local search and full local search hybrid GAs", which are constituted by using the "GAs and local search techniques" together, are introduced. The aim of this study is to make a systematic definition of the data is needed in the GA assisted solution of FLP and to present both a méthodologie frame and the bibliography in order to assist the studies that will be made on this subject.

Özet

Tesis Yerleşimi Problemi (TYP) konusunda çalışan ilk araştırmacılar yoğun olarak Kuadratik Atama Problemi (QAP-Quadratic Assignment Problem) üzerinde durmuşlardır. Ancak, QAP'nin NP-hard yapıda olması nedeniyle gerek aramaya çözüm uzayında "birden fazla noktadan başlanması" gerekse "modelde geometrik kısıt kullanımına gerek duyulmadığından" genetik algoritmalar (GAs), TYP'de başarıyla kullanılabilmektedir. Bu çalışmada önce TYP'nin GAs yardımıyla çözümü için gereksinim duyulan veri ve yaklaşımlar sistematik olarak incelenmiş, daha sonra GAs'ın TYP'nde nasıl kullanılabileceği konusunda metodolojik bir çerçeve sunulmaya çalışılmıştır. Yine QAP'nin çözümleme zamanını iyileştirmek için "GAs ile yerel arama (YA) teknikleri" beraber kullanılarak oluşturulan "kısmi aramalı ve tam yerel aramalı melez genetik algoritmalar" tanıtılmıştır. Bu çalışmanın amacı; TYP'nin GAs yardımıyla çözümü için gereksinim duyulan verilerin sistematik tanımlamasını yapmak ve bu konuda yapılacak çalışmalara yardımcı olabilmek için hem metodolojik bir çerçeve hem de bir bibliyografya sunmaktır.

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Cite This Article

TÜRKBEY, O. (2022). The genetic algorithms and theirs using in the facilities layout problem: Theory and methodology (Part II). Gazi Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 15 - Sayı: 2, 615–631. https://ajindex.com/articles/522287

References (2)

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