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Bulanık veri zarflama analizi ile beton pompası seçimi

Year 2017, Volume: 8 Issue: 1, 1 - 12, 01.03.2017

Abstract

Bu çalışmanın amacı,
inşaat sektöründeki makine seçimleri problemine, beton pompası açısından
yaklaşmak ve beton pompası üreticileri ve kullanıcılarına, üretim ve satın alma
süreçlerinde yardımcı olabilecek bir yöntem ortaya koymaktır. Bu kapsamda, çok
ölçütlü karar verme problemlerini çözmek için kullanılan yöntemlerden biri olan
veri zarflama analizi, elde edilen verilerin bazılarının bulanık sayılar
olmaları nedeniyle, bulanık veri zarflama analizi (BVZA) olarak kullanılmıştır.
Hesaplamalarda hem bulanık ve hem de normal sayıları aynı anda işleyebilmek
için Madea ve diğerleri (1998) tarafından geliştirilen ve Saati ve diğerleri
(2002) tarafından iyileştirilen α-kesim kümeleri yaklaşımından
faydalanılmıştır. Çalışma kapsamında, Türkiye’de faaliyet gösteren üç farklı
beton pompası üreticisi tarafından üretilen aynı tipteki birer adet beton
pompasının özellikleri ve beton pompası kullanıcısı 70 adet firmanın makine
sorumluları ile yapılan görüşmelerden elde edilen veriler kullanılarak, söz
konusu beton pompalarının etkinlikleri hesaplanmıştır. Bu etkinlik hesaplaması
sırasında Charnes-Cooper-Rhodes yöntemi ile elde edilen eşitlikler
kullanılmıştır.



Sonuç
olarak, değerlendirmeye alınan üç farklı beton pompası içerisinden B3 kodlu
beton pompası, etkinlik değeri en yüksek olan seçenek olarak belirlenmiştir. Bu
çalışma sonucunda elde edilen bulguların; beton pompası üreticilerinin
pazardaki durumlarını görmelerine, ürünlerini geliştirerek rekabet güçlerini
artırmalarına ve potansiyel alıcı ve kullanıcıların en uygun ürünü seçmelerine
yardımcı olabilecekleri düşünülmektedir. Ayrıca BVZA’nın, çok ölçütlü karar
verme problemlerinde karşılaşılan farklı tür ve birimlerdeki verilerin, mevcut
kısıtlarla birlikte değerlendirilmesinde kullanılabilecek oldukça faydalı bir yöntem
olduğu ortaya konulmuştur. Bu yöntem ile bir veya birden çok parametre
hedeflenerek hesaplamalar yapılabilmekte ve karar verme sürecinde avantaj
sağlanabilmektedir.

References

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  • Li, Q., Wang, K., ve Cross, S. (2013) Evaluation of warm mix asphalt (WMA): a case study, Proceedings, Airfield and Highway Pavement Conference, 118-127, Los Angeles.
  • Meada, Y., Entani, T. ve Tanaka, H. (1998). Fuzzy DEA with interval efficiency, Proceedings of the 6th European Congress on Intelligent Techniques and Soft Computing, 2, 1067–1071, Aachen.
  • Oruç, K. (2008). Veri zarflama analizi ile bulanik ortamda etkinlik ölçümleri ve üniversitelerde bir uygulama, Doktora Tezi, Süleyman Demirel Üniversitesi Sosyal Bilimler Enstitüsü, Isparta.
  • Ozbek, M., de la Garza, J. ve Triantis, K. (2010). Efficiency measurement of bridge maintenance using data envelopment analysis, Journal of. Infrastructe System, 16, 1, 31–39.
  • Saati, S. M. ve Memariani, A., (2005). Reducing weight flexibility in fuzzy DEA, Applied Mathematics and Computation, 161, 611–622.
  • Saati, S. M., Memariani, A. ve Jahanshahloo, G. R. (2002). Efficiency analysis and ranking of DMUs with fuzzy data, Fuzzy Optimization and Decision Making, 1, 3, 255–267.
  • Sengupta, K. J. (1992). A fuzzy system approach in data envelopment analysis, Computer Mathematics and Application, 24, 8/9, 259–266.
  • Tam, C. M., Tong, T. K. L., and Wong, Y. W. (2004). Selection of concrete pump using the superiority and inferiority ranking method, Journal of Construction Engineering and Management, 130, 6, 827–834.
  • Tatari, O. ve Kucukvar, M. (2012). Eco-Efficiency of construction materials: data envelopment analysis, Journal of Construction Engineering and Management, 138, 6, 733–741.
  • Taylan, O., Bafail, O. A, Abdulaal, M. S. R. ve Kabli, M. R. (2014). Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies, Applied Soft Computing, 17, 105 – 116.
  • Toprak, Z.F., (2009). Flow discharge modeling in open canals using a new fuzzy modeling technique (SMRGT), CLEAN-Soil, Air, Water, 37, 9, 742–752.
  • Toprak, Z.F. ve Cigizoglu, H.K., (2008). Predicting longitudinal dispersion coefficient in natural streams by artificial intelligence methods, Hydrological Processes, 22, 20, 4106-4129.
  • Toprak, Z.F. ve Savci, M.E., (2007). Longitudinal dispersion coefficient modeling in natural channels using fuzzy logic, CLEAN-Soil, Air, Water, 35, 6, 626-637.
  • Toprak, Z.F., Eris, E., Agiralioglu, N., Cigizoglu, H.K., Yilmaz, L., Aksoy, H., Coskun, G., Andic, G. ve Alganci, U., (2009). Modeling monthly mean flow in a poorly gauged basin by fuzzy logic, CLEAN-Soil, Air, Water, 37, 7, 555-564.
  • Toprak Z.F., Hamidi N, Kisi O, ve Gerger R., (2014). Modeling dimensionless longitudinal dispersion coefficient in natural streams using artificial intelligence methods, KSCE Journal of Civil Engineering March 2014, 18, 2, 718-730.
  • Torfi, F., Farahani, R. Z., ve Rezapour, S. (2010). Fuzzy AHP to determine the relative weights of evaluation criteria and Fuzzy TOPSIS to rank the alternatives, Applied Soft Computing, 10, 2, 520-528.
  • Ulubeyli, S. and Kazaz, A. (2009). A multiple criteria decision-making approach to the selection of concrete pumps, Journal of Civil Engineering and Management, 15, 4, 369–376.
  • Wang, C. H., Chuang, C. C. ve Tsai, C. C. (2009). A fuzzy DEA–Neural approach to measuring design service performance in PCM projects, Automation in Construction, 18, 702 – 713.
  • Zadeh, L. (1965), Fuzzy sets, Information and Control, 8, 338–353.
  • Zadeh, L. (1975), The concept of a linguistic variable and its application to approximate reasoning, Information Sciences, 8, 199–249.
  • Zhou, Z., Zhao, L , Lui, S. ve Ma, C. (2012). A generalized fuzzy DEA/AR performance assessment model, Mathematical and Computer Modelling, 55, 2117–2128.
Year 2017, Volume: 8 Issue: 1, 1 - 12, 01.03.2017

Abstract

References

  • Akkoyun Ö. ve Toprak Z.F., (2012). Fuzzy-based quality classification model for natural building stone blocks, Engineering Geology, 133–134, 66-75.
  • Aluclu, I., Dalgic, A. ve Toprak, Z.F., (2008). A fuzzy logic-based model for noise control at industrial workplaces, Applied Ergonomics, 39, 3, 368-378.
  • Chang, P. T. ve Lee, J. H (2012). A fuzzy DEA and knapsack formulation integrated model for project selection, Computers & Operations Research, 39, 112–125.
  • Coelli, T., Rao, D. ve Battese, D. (1998). An introduction to efficiency and productivity analysis, Kluwer Academic Publishers, Boston, Dordrecht, London.
  • Cook, W. D. ve Green, R. H. (2000). Project prioritization: A resource-constrained data envelopment analysis approach, Socio-Economic Planning Sciences, 34, 85–99.
  • Cooper, W., Seiford, L. ve Tone, K. (2000). Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software, Kluwer Academic Publishers, Boston, Dordrecht, London
  • El-Mashaleh, M. (2010). Decision to bid or not to bid: a data envelopment analysis approach, Canadian Journal of Civil Engineering, 37, 1, 37–44.
  • El-Mashaleh, M. S, Rababeh, S. M. ve Hyari, H. H. (2010). Utilizing data envelopment analysis to benchmark safety performance of construction contractors, International Journal of Project Management, 28, 61–67.
  • Guo, P. ve Tanaka, H. (2001). Fuzzy DEA: a perceptual evaluation method, Fuzzy Sets and Systems, 119, 149–160.
  • Kao, C. ve Liu, S. T.(200). Data envelopment analysis with missing data: an application to university libraries in Taiwan, The Journal of the Operational Research Society, 51, 8, 897–905.
  • Ksiazek, M., Nowak, P., Roslon, J., ve Wieczorek, T., (2014). Multicriteria assessment of selected solutions for the building structural walls, Proceedings, XXIII R-S-P seminar, Theoretical Foundation of Civil Engineering, 406 – 411, Wrocław.
  • Lertworasirikula, S., Fanga, S. C., Joinesb, J. A. ve. Nuttlea, H. L. (2003). Fuzzy data envelopment analysis (DEA): a possibility approach, Fuzzy Sets and Systems, 139, 2, 379–394.
  • Li, Q., Wang, K., ve Cross, S. (2013) Evaluation of warm mix asphalt (WMA): a case study, Proceedings, Airfield and Highway Pavement Conference, 118-127, Los Angeles.
  • Meada, Y., Entani, T. ve Tanaka, H. (1998). Fuzzy DEA with interval efficiency, Proceedings of the 6th European Congress on Intelligent Techniques and Soft Computing, 2, 1067–1071, Aachen.
  • Oruç, K. (2008). Veri zarflama analizi ile bulanik ortamda etkinlik ölçümleri ve üniversitelerde bir uygulama, Doktora Tezi, Süleyman Demirel Üniversitesi Sosyal Bilimler Enstitüsü, Isparta.
  • Ozbek, M., de la Garza, J. ve Triantis, K. (2010). Efficiency measurement of bridge maintenance using data envelopment analysis, Journal of. Infrastructe System, 16, 1, 31–39.
  • Saati, S. M. ve Memariani, A., (2005). Reducing weight flexibility in fuzzy DEA, Applied Mathematics and Computation, 161, 611–622.
  • Saati, S. M., Memariani, A. ve Jahanshahloo, G. R. (2002). Efficiency analysis and ranking of DMUs with fuzzy data, Fuzzy Optimization and Decision Making, 1, 3, 255–267.
  • Sengupta, K. J. (1992). A fuzzy system approach in data envelopment analysis, Computer Mathematics and Application, 24, 8/9, 259–266.
  • Tam, C. M., Tong, T. K. L., and Wong, Y. W. (2004). Selection of concrete pump using the superiority and inferiority ranking method, Journal of Construction Engineering and Management, 130, 6, 827–834.
  • Tatari, O. ve Kucukvar, M. (2012). Eco-Efficiency of construction materials: data envelopment analysis, Journal of Construction Engineering and Management, 138, 6, 733–741.
  • Taylan, O., Bafail, O. A, Abdulaal, M. S. R. ve Kabli, M. R. (2014). Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies, Applied Soft Computing, 17, 105 – 116.
  • Toprak, Z.F., (2009). Flow discharge modeling in open canals using a new fuzzy modeling technique (SMRGT), CLEAN-Soil, Air, Water, 37, 9, 742–752.
  • Toprak, Z.F. ve Cigizoglu, H.K., (2008). Predicting longitudinal dispersion coefficient in natural streams by artificial intelligence methods, Hydrological Processes, 22, 20, 4106-4129.
  • Toprak, Z.F. ve Savci, M.E., (2007). Longitudinal dispersion coefficient modeling in natural channels using fuzzy logic, CLEAN-Soil, Air, Water, 35, 6, 626-637.
  • Toprak, Z.F., Eris, E., Agiralioglu, N., Cigizoglu, H.K., Yilmaz, L., Aksoy, H., Coskun, G., Andic, G. ve Alganci, U., (2009). Modeling monthly mean flow in a poorly gauged basin by fuzzy logic, CLEAN-Soil, Air, Water, 37, 7, 555-564.
  • Toprak Z.F., Hamidi N, Kisi O, ve Gerger R., (2014). Modeling dimensionless longitudinal dispersion coefficient in natural streams using artificial intelligence methods, KSCE Journal of Civil Engineering March 2014, 18, 2, 718-730.
  • Torfi, F., Farahani, R. Z., ve Rezapour, S. (2010). Fuzzy AHP to determine the relative weights of evaluation criteria and Fuzzy TOPSIS to rank the alternatives, Applied Soft Computing, 10, 2, 520-528.
  • Ulubeyli, S. and Kazaz, A. (2009). A multiple criteria decision-making approach to the selection of concrete pumps, Journal of Civil Engineering and Management, 15, 4, 369–376.
  • Wang, C. H., Chuang, C. C. ve Tsai, C. C. (2009). A fuzzy DEA–Neural approach to measuring design service performance in PCM projects, Automation in Construction, 18, 702 – 713.
  • Zadeh, L. (1965), Fuzzy sets, Information and Control, 8, 338–353.
  • Zadeh, L. (1975), The concept of a linguistic variable and its application to approximate reasoning, Information Sciences, 8, 199–249.
  • Zhou, Z., Zhao, L , Lui, S. ve Ma, C. (2012). A generalized fuzzy DEA/AR performance assessment model, Mathematical and Computer Modelling, 55, 2117–2128.
There are 33 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Volkan Arslan

Serdar Ulubeyli This is me

Publication Date March 1, 2017
Submission Date November 2, 2015
Published in Issue Year 2017 Volume: 8 Issue: 1

Cite

IEEE V. Arslan and S. Ulubeyli, “Bulanık veri zarflama analizi ile beton pompası seçimi”, DUJE, vol. 8, no. 1, pp. 1–12, 2017.
DUJE tarafından yayınlanan tüm makaleler, Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır. Bu, orijinal eser ve kaynağın uygun şekilde belirtilmesi koşuluyla, herkesin eseri kopyalamasına, yeniden dağıtmasına, yeniden düzenlemesine, iletmesine ve uyarlamasına izin verir. 24456