Journals / International Journal of Advances in Engineering and Pure Sciences / 2015 / Cilt: 27 - Sayı: 4

A Fuzzy Logic Evaluation Model To Select A Site Chief For Construction Projects

İnşaat Projeleri İçin Teknik Personel Seçiminde Bir Bulanık Mantık Değerlendirme Modeli

Pages
104–113
Publication Date
2016-05-06
DOI
—
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AI Summary & Key Takeaways

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

Thus, a construction project will be able to be completed in time, within desired quality and budget by using this model.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:A construction company struggles to deliver the undertaken project within time, budget, and desired quality.
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Key Findings & Data:Selection of technical personnel is generally made on the basis of a decision maker’s subjective evaluation, and includes fuzzy linguistic variables; therefore, the best suitable candidate cannot be seen easily.
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Conclusion & Impact:Thus, a construction project will be able to be completed in time, within desired quality and budget by using this model.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:İnşaat Yönetimi · Bulanık Mantık · İnsan Kaynakları

Abstract

A construction company struggles to deliver the undertaken project within time, budget, and desired quality. Selection of technical personnel is generally made on the basis of a decision maker’s subjective evaluation, and includes fuzzy linguistic variables; therefore, the best suitable candidate cannot be seen easily. The aim of this study is to develop a fuzzy logic evaluation model to be able to select appropriate technical personnel. For this purpose, it is determined decision criteria, the evaluation fuzzy set and the affect fuzzy set in a construction project for selecting site chief. Thus, a construction project will be able to be completed in time, within desired quality and budget by using this model.

Özet

Bir inşaat firması, taahhüdü altındaki bir projeyi, zamanında, bütçesinde ve istenilen kalitede bitirmek ister. Teknik personel de firmanın bu amacını gerçekleştirmede anahtar bir rol oynamaktadır. Teknik personelin seçimi genellikle, firmadaki personel seçiminden sorumlu kişilerin subjektif değerlendirmelerine göre yapılmaktadır. Personel seçiminde subjektif değerlendirme yönteminin uygulanması, proje için en uygun adayın seçilememesine sebebiyet vermektedir. Bu çalışmada, personel seçim probleminin çözümü için bir bulanık mantık değerlendirme modeli geliştirilip, geliştirilen model, gerçek bir inşaat projesi için anahtar teknik personel olan şantiye şefi seçiminde uygulanarak, uygulama ile elde edilen sonuçlar; şantiye şefi seçim problemi için önerilen yöntemin kullanılmasının etkili ve yararlı bir yöntem olduğunu göstermektedir.

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Cite This Article

ANBARCI, M., ÖZ, B. (2016). A Fuzzy Logic Evaluation Model To Select A Site Chief For Construction Projects. International Journal of Advances in Engineering and Pure Sciences, Cilt: 27 - Sayı: 4, 104–113. https://ajindex.com/articles/257879

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References (16)

  1. Anbarcı, M., Manisalı, E., Konut Projeleri Şantiye Şefi Seçimi, Yapı Dünyası Sayı:190, 12-16, 2012.
  2. Gilan, S.S., Sebt, M.H., Shahhosseini,V., Computing with words for hierarchical competency based selection of personnel in construction companies, Applied Soft Computing, Applied Soft Computing, 12, 860–871, 2012. Dağdeviren,M., Bulanık analitik hiyerarşi prosesi ile personel seçimi ve bir uygulama, Gazi Üniv. Müh. Mim. Fak. Der., Cilt 22, No 4, 791-799, 2007.
  3. Özgörmüş, E., Mutlu, Ö., Güner, H., 2005,
  4. Bulanık Ahp ile Personel Seçimi, V.Ulusal Üretim Araştırmaları Sempozyumu, İstanbul Ticaret Üniversitesi, 25-27
  5. Kasım. Yıldız, A., Deveci, M., 2013, Bulanık VIKOR Yöntemine Dayalı Personel Seçim Süreci, Ege Akademik Bakış, Cilt 13,427-436.
  6. Ross, T.J., Fuzzy logic with engineering applications, McGraw-Hill, Singapore, 1997.
  7. Zadeh, L.A., Fuzzy sets, Informatic and control, Cilt 8, No 3, 338-353, 1965.
  8. Klir, G.J., Clair, U.H., Yuan, B., Fuzzy Set Theory: Foundations and Applications, Prentice Hall PTR, Upper Saddle River, NJ, ABD, 1997.
  9. Klir, G.J. and Yuan, B., Fuzzy Sets and Fuzzy Logicy: Theory and Applications, Prentice Hall Inc, Englewood Cliffs, NJ, ABD, 1997.
  10. Reznik, L., Fuzzy controllers, Biddles Ltd., Guildford and King’s Lynn, 1997.
  11. The Mathworks, Inc., Fuzzy Logic Toolbox, User’s Guide R2014b, 3 Apple Hill Drive Natick, ABD, 2014.
  12. Kusiak, A., 2011, Fuzzy logic [on line], Intelligent Systems laboratory 2139 Seamans Systems, The University of Iowa, Iowa City, Iowa 52242-1257, ABD,http://css.engineering.uiowa.edu/~comp/ Public/Fuzzy_logic_2.pdf [15 Subat 2012].
  13. Wu, X., 2011, Fuzzy rule based systems [online], Jiangnan University, Jiangsu, Çin, http://course. cmjnu.com.cn/courses/03014a/content/syjx/dzja/ AppendixChapter5.swf [15 Şubat 2012].
  14. El-Sharkawi, M. A., 2011, Fuzzy systems [online], Department of Electrical Engineering, University of Washington, Seattle, WA 98195-2500, USA, http:// cialab.ee.washington.edu/index_files/Page598.html [01 Haziran 2011].
  15. Schmidt, S., Steele, R., and Dillon, T., 2012, Towards usage policies for fuzzy inference methodologies for trust and QoS assessment [on line], University of Technology, Sydney,PO Box 123 Broadway, NSW 2007Australia, http://epress.lib.uts.edu.au/ research/bitstream/handle/10453/2397/2006005298. pdf?sequence=1 [15 Subat 2012].
  16. Jang, J.-S. R. ve C.-T. Sun, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall, 1997. A. Kandel, editor. Fuzzy expert syst