Kümeleme Tabanlı Genetik Algoritmayla Tesis Yeri Seçimi

Yıl 2020, Cilt: 5 Sayı: 2, 90 - 98, 24.06.2020

Yakup Çelikbilek

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

Cited By: 1

Öz

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.

Anahtar Kelimeler

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

Facility Location Selection Using Clustering Based Genetic Algorithm

Öz

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.

Anahtar Kelimeler

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

Kaynakça

• Arogundade, O. T., Akinwale, A. T., Adekoya, A. F. and Awe Oludare, G. (2005) A 0-1 model for fire and emergency service facility location selection: a case study in Nigeria, J. Theor. Appl. Inf. Tech, 9, 50-59.
• Athawale, V. M. and Chakraborty, S. (2010, January) Facility location selection using PROMETHEE II method, In Proceedings of the 2010 international conference on industrial engineering and operations management, 9-10, Bangladesh Dhaka.
• Bolturk, E. and Kahraman, C. (2018) Interval-valued intuitionistic fuzzy CODAS method and its application to wave energy facility location selection problem, Journal of Intelligent & Fuzzy Systems, 35(4), 4865-4877.
• Chou, S. Y., Chang, Y. H. and Shen, C. Y. (2008) A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes, European Journal of Operational Research, 189(1), 132-145.
• Çebi, F. and Otay, İ. (2015) Multi-criteria and multi-stage facility location selection under interval type-2 fuzzy environment: a case study for a cement factory, international Journal of computational intelligence systems, 8(2), 330-344.
• Ertuğrul, İ. and Karakaşoğlu, N. (2008) Comparison of fuzzy AHP and fuzzy TOPSIS methods for facility location selection, The International Journal of Advanced Manufacturing Technology, 39(7-8), 783-795.
• Holland, J. H. (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, Ann Arbor, University of Michigan Press.
• Kabir, G. and Sumi, R. S. (2013) Integrating modified Delphi with fuzzy AHP for concrete production facility location selection, International Journal of Fuzzy System Applications (IJFSA), 3(3), 68-81.
• Kahraman, C., Ruan, D. and Doǧan, I. (2003) Fuzzy group decision-making for facility location selection, Information sciences, 157, 135-153.
• Kaul, A., Darbari, J. D. and Jha, P. C. (2020) A Fuzzy MCDM Model for Facility Location Evaluation Based on Quality of Life, In Soft Computing for Problem Solving, 687-697, Springer, Singapore.
• Kaya, I. and Çinar, D. (2006) Facility location selection using a fuzzy outranking method, In Applied Artificial Intelligence, 359-366.
• Kheybari, S., Kazemi, M. and Rezaei, J. (2019) Bioethanol facility location selection using best-worst method, Applied energy, 242, 612-623.
• MacQueen, J. (1967, June) Some methods for classification and analysis of multivariate observations, In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1(14), 281-297.
• Mladenovic, N., Moreno, J. A. and Moreno-Vega, J. M. (1996) Chain-interchange heuristic method, Yugosl J Oper Res, 6(1), 41-54.
• Mousavi, S. M., Gitinavard, H., Vahdani, B. and Foroozesh, N. (2019) Hierarchical group compromise ranking methodology based on Euclidean–Hausdorff distance measure under uncertainty: An application to facility location selection problem, Journal of Optimization in Industrial Engineering, 12(2), 93-105.
• OR Library (2019) Location Problems, Multi Source Weber Problems. http://mistic.heig-vd.ch/taillard/problemes.dir/location.html. Access Date: 15.02.2020.
• Rahman, M. S., Ali, M. I., Hossain, U. and Mondal, T. K. (2018) Facility location selection for plastic manufacturing industry in Bangladesh by using AHP method, International Journal of Research in Industrial Engineering, 7(3), 307-319.
• Ray, A., De, A. and Dan, P. K. (2015) Facility location selection using complete and partial ranking MCDM methods, International Journal of Industrial and Systems Engineering, 19(2), 262-276.
• Ruspini, E. H. (1970) Numerical methods for fuzzy clustering, Information Sciences, 2(3), 319-350.
• Safari, H., Faghih, A. and Fathi, M. R. (2012). Fuzzy multi-criteria decision making method for facility location selection. African Journal of Business Management, 6(1), 206.
• Seker, S. and Aydin, N. (2020) Hydrogen production facility location selection for Black Sea using entropy based TOPSIS under IVPF environment, International Journal of Hydrogen Energy, doi: 10.1016/j.ijhydene.2019.12.183.
• Shen, C. Y. and Yu, K. T. (2009) A generalized fuzzy approach for strategic problems: The empirical study on facility location selection of authors’ management consultation client as an example, Expert Systems with Applications, 36(3), 4709-4716.
• Singh, R. K. (2016) Facility location selection using extent fuzzy AHP, International Advanced Research Journal in Science, Engineering and Technology, 3(2), 47–51.
• Temur, G. T., Kaya, T. and Kahraman, C. (2014) Facility location selection in reverse logistics using a type-2 fuzzy decision aid method, In Supply chain management under fuzziness, 591-606, Springer, Berlin, Heidelberg.
• Verter, V. and Dincer, M. C. (1992) An integrated evaluation of facility location, capacity acquisition, and technology selection for designing global manufacturing strategies, European Journal of Operational Research, 60(1), 1-18.
• Weber, A. and Friedrich C. J. (1962) Theory of the location of industries, Chicago, Illinois: The University of Chicago Press.