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

Year 2015, Volume: 27 Issue: 4, 104 - 113, 06.05.2016
https://doi.org/10.7240/mufbed.30344

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. 

References

  • Anbarcı, M., Manisalı, E., Konut Projeleri Şantiye Şefi Seçimi, Yapı Dünyası Sayı:190, 12-16, 2012.
  • 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. Özgörmüş, E., Mutlu, Ö., Güner, H., 2005, Bulanık Ahp ile Personel Seçimi, V.Ulusal Üretim Araştırmaları Sempozyumu, İstanbul Ticaret Üniversitesi, 25-27 Kasım. Yıldız, A., Deveci, M., 2013, Bulanık VIKOR Yöntemine Dayalı Personel Seçim Süreci, Ege Akademik Bakış, Cilt 13,427-436.
  • Ross, T.J., Fuzzy logic with engineering applications, McGraw-Hill, Singapore, 1997.
  • Zadeh, L.A., Fuzzy sets, Informatic and control, Cilt 8, No 3, 338-353, 1965.
  • Klir, G.J., Clair, U.H., Yuan, B., Fuzzy Set Theory: Foundations and Applications, Prentice Hall PTR, Upper Saddle River, NJ, ABD, 1997.
  • Klir, G.J. and Yuan, B., Fuzzy Sets and Fuzzy Logicy: Theory and Applications, Prentice Hall Inc, Englewood Cliffs, NJ, ABD, 1997.
  • Reznik, L., Fuzzy controllers, Biddles Ltd., Guildford and King’s Lynn, 1997.
  • The Mathworks, Inc., Fuzzy Logic Toolbox, User’s Guide R2014b, 3 Apple Hill Drive Natick, ABD, 2014.
  • 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].
  • 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].
  • 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].
  • 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].
  • 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 systems. CRC Press, Inc., Boca Raton, FL, 1992.
  • Osofısan, P.B., “Fuzzy logic control of the syrup mixing process in beverage production”, Leonardo journal of sciences, No 11, 93-108, 2007.
  • B. Kosko. Neural networks and fuzzy systems: A dynamical systems approach. Prentice Hall, Upper Saddle River, NJ, 1991.
  • Lee C.C. Fuzzy logic in control systems: Fuzzy logic controller-part 1. IEEE Trans actions on Systems, Man, and Cybernetics, 1990.
  • Lee C.C. Fuzzy logic in control systems: fuzzy logic controller-part 2. IEEE Trans actions on Systems, Man, and Cybernetics, 1990.
  • E. H. Mamdani and S. Assilian. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 1975.
  • Pant, S.N. and Holbert, K.E., Fuzzy rules and implication [online], Fulton, Arizona, ABD,http:// enpub.fulton.asu.edu/PowerZone/FuzzyLogic/ chapter%205/frame5.htm [13 Mart 2012].
  • Cook, P.R., 2011, Fuzzy inference system (Sugeno) [online], Department of Computer Science, Princeton University, New Jersey 08544, ABD,
  • http://www.cs.princeton.edu/courses/archive/fall07/ cos436/HIDDEN/Knapp/fuzzy004.htm [15 Şubat 2012].
  • M. Sugeno and G. T. Kang. Structure identification of fuzzy model. Fuzzy Sets and Systems, 1988.
  • T. Takagi and M. Sugeno. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 1985.
  • Özbayoğlu, M.A., 2011, Bulanık uzman sistemler [on line], Bilgisayar Mühendisliği Bölümü TOBB Ekonomi ve Teknoloji Üniversitesi, Ankara, http:// mozbayoglu.etu.edu.tr/Dersler/Bahar_2010/bil441/ bulanik_uzman_sistemler.ppt [15 Subat 2012].
  • Jang, J.-S. R. and C.-T. Sun, “Neuro-fuzzy modeling and control, Proceedings of the IEEE, 1995.
  • Huang, D.K., Chiu, H.N., Yeh, R.H., Chang, J.H., 2009, A fuzzy multi-criteria decision making approach for solving a bi-objective personnel assignment problem, Computers&Industrial Engineering 56, 1-10
  • Özkök, A.F., Kozanoğlu, O., 2009, Takım Lideri Seçiminde Bulanık Kalite Fonksiyonu Açınımı Modeli Uygulaması, Journal of Yasar University, 4(15), 2403-2418
  • Lin, H.T., 2010, Personnel selection using analytic network process and fuzzy data envelopment analysis approaches, Computers&Industrial Engineering, 59, 937-944.
  • Kelemenis, A., Askounis, D., 2010, A new TOPSIS- based multi-criteria approach to personnel selection, Expert Systems with applications, 37,4999-5008
  • Dursun, M., Karsak, E.E., 2010, A fuzzy MCDM approach for personnel selection, Expert systems with applications, 37, 4324-4330
  • Rashidi, A., Jazebi, F., Brilakis, I., 2011, Neurofuzzy Genetic System for Selection of Construction Project Managers, Journal of Construction Engineering and Managemet, 17-29
  • Kelemenis, A., Ergazakis, K., Askounis,D., 2011, Support Managers’ selection using an extention of fuzzy TOPSIS, Expert Systems with Applications, 38, 2774-2782
  • Gilan, S.S., Sebt, M.H., Shahhosseini, V., 2012, Computing with words for hierarchical competency based selection of personnel in construction companies, Applied Soft Computing, 12,860-871

İnşaat Projeleri İçin Teknik Personel Seçiminde Bir Bulanık Mantık Değerlendirme Modeli

Year 2015, Volume: 27 Issue: 4, 104 - 113, 06.05.2016
https://doi.org/10.7240/mufbed.30344

Abstract

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.

References

  • Anbarcı, M., Manisalı, E., Konut Projeleri Şantiye Şefi Seçimi, Yapı Dünyası Sayı:190, 12-16, 2012.
  • 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. Özgörmüş, E., Mutlu, Ö., Güner, H., 2005, Bulanık Ahp ile Personel Seçimi, V.Ulusal Üretim Araştırmaları Sempozyumu, İstanbul Ticaret Üniversitesi, 25-27 Kasım. Yıldız, A., Deveci, M., 2013, Bulanık VIKOR Yöntemine Dayalı Personel Seçim Süreci, Ege Akademik Bakış, Cilt 13,427-436.
  • Ross, T.J., Fuzzy logic with engineering applications, McGraw-Hill, Singapore, 1997.
  • Zadeh, L.A., Fuzzy sets, Informatic and control, Cilt 8, No 3, 338-353, 1965.
  • Klir, G.J., Clair, U.H., Yuan, B., Fuzzy Set Theory: Foundations and Applications, Prentice Hall PTR, Upper Saddle River, NJ, ABD, 1997.
  • Klir, G.J. and Yuan, B., Fuzzy Sets and Fuzzy Logicy: Theory and Applications, Prentice Hall Inc, Englewood Cliffs, NJ, ABD, 1997.
  • Reznik, L., Fuzzy controllers, Biddles Ltd., Guildford and King’s Lynn, 1997.
  • The Mathworks, Inc., Fuzzy Logic Toolbox, User’s Guide R2014b, 3 Apple Hill Drive Natick, ABD, 2014.
  • 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].
  • 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].
  • 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].
  • 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].
  • 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 systems. CRC Press, Inc., Boca Raton, FL, 1992.
  • Osofısan, P.B., “Fuzzy logic control of the syrup mixing process in beverage production”, Leonardo journal of sciences, No 11, 93-108, 2007.
  • B. Kosko. Neural networks and fuzzy systems: A dynamical systems approach. Prentice Hall, Upper Saddle River, NJ, 1991.
  • Lee C.C. Fuzzy logic in control systems: Fuzzy logic controller-part 1. IEEE Trans actions on Systems, Man, and Cybernetics, 1990.
  • Lee C.C. Fuzzy logic in control systems: fuzzy logic controller-part 2. IEEE Trans actions on Systems, Man, and Cybernetics, 1990.
  • E. H. Mamdani and S. Assilian. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 1975.
  • Pant, S.N. and Holbert, K.E., Fuzzy rules and implication [online], Fulton, Arizona, ABD,http:// enpub.fulton.asu.edu/PowerZone/FuzzyLogic/ chapter%205/frame5.htm [13 Mart 2012].
  • Cook, P.R., 2011, Fuzzy inference system (Sugeno) [online], Department of Computer Science, Princeton University, New Jersey 08544, ABD,
  • http://www.cs.princeton.edu/courses/archive/fall07/ cos436/HIDDEN/Knapp/fuzzy004.htm [15 Şubat 2012].
  • M. Sugeno and G. T. Kang. Structure identification of fuzzy model. Fuzzy Sets and Systems, 1988.
  • T. Takagi and M. Sugeno. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 1985.
  • Özbayoğlu, M.A., 2011, Bulanık uzman sistemler [on line], Bilgisayar Mühendisliği Bölümü TOBB Ekonomi ve Teknoloji Üniversitesi, Ankara, http:// mozbayoglu.etu.edu.tr/Dersler/Bahar_2010/bil441/ bulanik_uzman_sistemler.ppt [15 Subat 2012].
  • Jang, J.-S. R. and C.-T. Sun, “Neuro-fuzzy modeling and control, Proceedings of the IEEE, 1995.
  • Huang, D.K., Chiu, H.N., Yeh, R.H., Chang, J.H., 2009, A fuzzy multi-criteria decision making approach for solving a bi-objective personnel assignment problem, Computers&Industrial Engineering 56, 1-10
  • Özkök, A.F., Kozanoğlu, O., 2009, Takım Lideri Seçiminde Bulanık Kalite Fonksiyonu Açınımı Modeli Uygulaması, Journal of Yasar University, 4(15), 2403-2418
  • Lin, H.T., 2010, Personnel selection using analytic network process and fuzzy data envelopment analysis approaches, Computers&Industrial Engineering, 59, 937-944.
  • Kelemenis, A., Askounis, D., 2010, A new TOPSIS- based multi-criteria approach to personnel selection, Expert Systems with applications, 37,4999-5008
  • Dursun, M., Karsak, E.E., 2010, A fuzzy MCDM approach for personnel selection, Expert systems with applications, 37, 4324-4330
  • Rashidi, A., Jazebi, F., Brilakis, I., 2011, Neurofuzzy Genetic System for Selection of Construction Project Managers, Journal of Construction Engineering and Managemet, 17-29
  • Kelemenis, A., Ergazakis, K., Askounis,D., 2011, Support Managers’ selection using an extention of fuzzy TOPSIS, Expert Systems with Applications, 38, 2774-2782
  • Gilan, S.S., Sebt, M.H., Shahhosseini, V., 2012, Computing with words for hierarchical competency based selection of personnel in construction companies, Applied Soft Computing, 12,860-871
There are 35 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Research Articles
Authors

Murat Anbarcı

Burak Öz

Publication Date May 6, 2016
Published in Issue Year 2015 Volume: 27 Issue: 4

Cite

APA Anbarcı, M., & Öz, B. (2016). İnşaat Projeleri İçin Teknik Personel Seçiminde Bir Bulanık Mantık Değerlendirme Modeli. Marmara Fen Bilimleri Dergisi, 27(4), 104-113. https://doi.org/10.7240/mufbed.30344
AMA Anbarcı M, Öz B. İnşaat Projeleri İçin Teknik Personel Seçiminde Bir Bulanık Mantık Değerlendirme Modeli. MFBD. May 2016;27(4):104-113. doi:10.7240/mufbed.30344
Chicago Anbarcı, Murat, and Burak Öz. “İnşaat Projeleri İçin Teknik Personel Seçiminde Bir Bulanık Mantık Değerlendirme Modeli”. Marmara Fen Bilimleri Dergisi 27, no. 4 (May 2016): 104-13. https://doi.org/10.7240/mufbed.30344.
EndNote Anbarcı M, Öz B (May 1, 2016) İnşaat Projeleri İçin Teknik Personel Seçiminde Bir Bulanık Mantık Değerlendirme Modeli. Marmara Fen Bilimleri Dergisi 27 4 104–113.
IEEE M. Anbarcı and B. Öz, “İnşaat Projeleri İçin Teknik Personel Seçiminde Bir Bulanık Mantık Değerlendirme Modeli”, MFBD, vol. 27, no. 4, pp. 104–113, 2016, doi: 10.7240/mufbed.30344.
ISNAD Anbarcı, Murat - Öz, Burak. “İnşaat Projeleri İçin Teknik Personel Seçiminde Bir Bulanık Mantık Değerlendirme Modeli”. Marmara Fen Bilimleri Dergisi 27/4 (May 2016), 104-113. https://doi.org/10.7240/mufbed.30344.
JAMA Anbarcı M, Öz B. İnşaat Projeleri İçin Teknik Personel Seçiminde Bir Bulanık Mantık Değerlendirme Modeli. MFBD. 2016;27:104–113.
MLA Anbarcı, Murat and Burak Öz. “İnşaat Projeleri İçin Teknik Personel Seçiminde Bir Bulanık Mantık Değerlendirme Modeli”. Marmara Fen Bilimleri Dergisi, vol. 27, no. 4, 2016, pp. 104-13, doi:10.7240/mufbed.30344.
Vancouver Anbarcı M, Öz B. İnşaat Projeleri İçin Teknik Personel Seçiminde Bir Bulanık Mantık Değerlendirme Modeli. MFBD. 2016;27(4):104-13.

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