Kümeleme Tabanlı Genetik Algoritmayla Tesis Yeri Seçimi

Year 2020, Volume: 5 Issue: 2, 90 - 98, 24.06.2020

Yakup Çelikbilek

https://doi.org/10.23834/isrjournal.689861

Cited By: 1

Abstract

Tesis yeri seçimi, şirketlerin ve endüstrilerin en önemli kararlarından biridir. Aynı zamanda, bir iş süreci tesis yeri seçimi ile başladığından, üzerinde durulması gereken ilk adımdır. Eğer tedarikçilere, üreticilere veya pazara uzak bir konum seçilirse; bu durum, hem şirket hem de tedarik ağındaki diğer öğeler açısından uzun vadede artan maliyetlere neden olacaktır. Ayrıca uzak konum, karşılıklı yapılan sözleşmeleri de detaylı olarak etkileyecektir. Bununla birlikte tesis yeri, işgücü maliyetleri ve diğer maliyetler üzerinde de etkilidir. Şirketteki maliyetlerin neredeyse tamamı tesisin konumu ile yakından alakalıdır. Bahsedilen bu öneminden yola çıkarak, bu çalışmada tesis yeri seçimi problemi ele alınmakta ve problemin çözümü için, kümeleme tabanlı genetik algoritma yöntemi önerilmektedir. Çalışmanın, giriş bölümünde, tesis yeri seçimi problemi ve ilgili literatür tanıtılmaktadır. Ardından; çözümde kullanılan yöntemler, K-ortalamalar kümeleme algoritması, genetik algoritma ve önerilen algoritma olarak sırasıyla sunulmaktadır. Çalışmanın ayrıntılı sayısal sonuçları, Yöneylem Araştırması Kütüphanesi’nden Ruspini75 veri seti kullanılarak tesis yeri seçimi bölümünde verilmektedir. Tartışma ve sonuçlar bölümünde, elde edilen tüm sonuçlar yorumlanmakta ve gelecekteki çalışmalar için öneriler ile çalışma sonuçlandırılmaktadır.

Keywords

Tesis Yeri, Tesis Yeri Seçimi, Kümeleme, Genetik Algoritma, Sezgiseller

Facility Location Selection Using Clustering Based Genetic Algorithm

Abstract

Facility location selection is one of the most important decisions of companies and industries. At the same time, since a business process begins with the selection of a facility location, it is the first step to consider. Everything starts with the facility location selection. If a location far to suppliers, manufacturers or the market is selected, this will lead to increasing costs in the long run for both the company and other items in the supply network. The distant location also affects the mutual contracts in detail. Besides, the facility location has effects on labor costs and other related costs. Almost all of the costs in the company is closely related with the facility location. Based on this mentioned importance, the facility location selection problem is considered in this study, and the clustering based genetic algorithm method is proposed for the solution of facility location selection problem. In the introductory part of the study, facility location selection problem and the related literature is introduced. After, methods used in the solution are presented as K-means clustering algorithm, genetic algorithm and the proposed algorithm respectively. Detailed numerical results of the study is given in the facility location selection section by using Ruspini75 data set from Operations Research Library. All obtained results are interpreted in the results and discussion section and the study is concluded with the suggestions for future works.

Keywords

Facility Location, Facility Location Selection, Clustering, Genetic Algorithm, Heuristics

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