Ali Osman KUŞAKCI
(İbn Haldun Üniversitesi, Yönetim Bilimleri Fakültesi, İşletme Bölümü, İstanbul, Türkiye)
Yıl: 2019Cilt: 11Sayı: 1ISSN: 1308-5514 / 1308-5514Sayfa Aralığı: 99 - 108Türkçe

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Hibritlenmiş Fuzzy-AHP ve TOPSIS Yöntemi İle Ürün Seçimi için Bir Karar Destek Sistemi
Ürün gamının çok geniş olduğu ürün aileleleri için talep edilen ürünün müşterinin isteği doğrultusunda; maliyet, kalite,fonksiyonellik gibi müşterinin ihtiyaçlarına/önceliklerine en iyi cevap verebilecek şekilde seçilmesi süreci karmaşık ve zahmetlibir Çok Kriterli Karar Verme (ÇKKV) problemidir. Bu çalışmada, Bulanık-AHP ve TOPSIS metotlarını kullanarak endüstriyeltip fan seçimi problemi için hibrit bir karar destek sistemi önerilmektedir. Önerilen model ile müşterinin taleplerine veönceliklerine göre kriter ağırlıklarının Bulanık-AHP ile tespiti yapılmaktadır. Elde edilen kriter ağırlıkları kullanılarak TOPSISyöntemi ile en iyi alternatifler sıralanmakta ve müşteriye sunulmaktadır
DergiAraştırma MakalesiErişime Açık
  • Abdullah, L., & Zulkifli, N. (2015). Integration of fuzzy AHP and interval type-2 fuzzy DEMATEL: An application to human resource management. Expert Systems with Applications, 42(9), 4397–4409. https://doi.org/10.1016/j.eswa.2015.01.021
  • Ahmed Ali, B. A., Sapuan, S. M., Zainudin, E. S., & Othman, M. (2015). Implementation of the expert decision system for environmental assessment in composite materials selection for automotive components. Journal of Cleaner Production, 107, 557–567. https://doi.org/10.1016/j.jclepro.2015.05.084
  • Al-Oqla, F. M., & Salit, M. S. (2017). Material selection of natural fiber composites using the analytical hierarchy process. In Materials Selection for Natural Fiber Composites (pp. 169–234). Elsevier. https://doi.org/10.1016/B978-0-08-100958-1.00006-2
  • Aydin, N., Celik, E., & Gumus, A. T. (2015). A hierarchical customer satisfaction framework for evaluating rail transit systems of Istanbul. Transportation Research Part A: Policy and Practice, 77, 61–81. https://doi.org/10.1016/j.tra.2015.03.029
  • Ayhan, M. B. (2013). A Fuzzy AHP Approach for Supplier Selection Problem: A Case Study in a Gearmotor Company. Internation Journal of Managing Value and Supply Chains (IJMVSC), 4(3), 11–23. https://doi.org/10.5121/ijmvsc.2013.4302
  • Ayvaz, B., & Kuşakcı, A. O. (2017). A trapezoidal type-2 fuzzy multi-criteria decision making method based on TOPSIS for supplier selection. Pamukkale University Journal of Engineering Sciences, 23(1), 71–80. https://doi.org/10.5505/pajes.2016.56563
  • Balo, F., & Şağbanşua, L. (2016). The Selection of the Best Solar Panel for the Photovoltaic System Design by Using AHP. Energy Procedia, 100, 50–53. https://doi.org/10.1016/j.egypro.2016.10.151
  • Baykal, N., & Beyan, T. (2004). Bulanık Mantık İlke Ve Temelleri (1st ed.). İstanbul: Seçkin.
  • Behzadian, M., Khanmohammadi Otaghsara, S., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051–13069. https://doi.org/10.1016/j.eswa.2012.05.056
  • Buckley, J. J. (1985). Ranking alternatives using fuzzy numbers. Fuzzy Sets and Systems, 15(1), 21–31. https://doi.org/10.1016/0165-0114(85)90013-2
  • Bulut, E., Duru, O., & Kocak, G. (2015). Rotational priority investigation in fuzzy analytic hierarchy process design: An empirical study on the marine engine selection problem. Applied Mathematical Modelling, 39(2), 913–923. https://doi.org/10.1016/j.apm.2014.07.018
  • Caputo, A. C., Pelagagge, P. M., & Salini, P. (2013). AHP-based methodology for selecting safety devices of industrial machinery. Safety Science, 53, 202–218. https://doi.org/10.1016/j.ssci.2012.10.006
  • Celik, E., Gul, M., Aydin, N., Gumus, A. T., & Guneri, A. F. (2015). A comprehensive review of multi criteria decision making approaches based on interval type-2 fuzzy sets. Knowledge-Based Systems, 85, 329–341. https://doi.org/10.1016/j.knosys.2015.06.004
  • Dožić, S., & Kalić, M. (2015). Comparison of Two MCDM Methodologies in Aircraft Type Selection Problem. Transportation Research Procedia, 10, 910–919. https://doi.org/10.1016/j.trpro.2015.09.044
  • Durán, O., & Aguilo, J. (2008). Computer-aided machine-tool selection based on a Fuzzy-AHP approach. Expert Systems with Applications, 34(3), 1787–1794. https://doi.org/10.1016/j.eswa.2007.01.046
  • Erdoğan, M., & Kaya, İ. (2016). Evaluating Alternative-Fuel Busses for Public Transportation in Istanbul Using Interval Type2 Fuzzy AHP and TOPSIS. Journal of Multiple-Valued Logic & Soft Computing, 26(6), 625. Retrieved from http://ezproxy.ticaret.edu.tr/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=edb&AN=116399297&site=eds -live
  • Görener, A., Ayvaz, B., Kusakci, A. O., & Altinok, E. (2017). A hybrid type-2 fuzzy based supplier performance evaluation methodology : The Turkish Airlines technic case. Applied Soft Computing, 56(1), 436–445. https://doi.org/10.1016/j.asoc.2017.03.026
  • Gul, M., Celik, E., Gumus, A. T., & Guneri, A. F. (2017). A fuzzy logic based PROMETHEE method for material selection problems. Beni-Suef University Journal of Basic and Applied Sciences. https://doi.org/10.1016/j.bjbas.2017.07.002
  • Kilic, H. S., Zaim, S., & Delen, D. (2014). Development of a hybrid methodology for ERP system selection: The case of Turkish Airlines. Decision Support Systems, 66, 82–92. https://doi.org/10.1016/j.dss.2014.06.011
  • Krohling, R. A., & Pacheco, A. G. C. (2015). A-TOPSIS – An Approach Based on TOPSIS for Ranking Evolutionary Algorithms. Procedia Computer Science, 55(1), 308–317. https://doi.org/10.1016/j.procs.2015.07.054
  • Lai, Y.-J., Liu, T.-Y., & Hwang, C.-L. (1994). TOPSIS for MODM. European Journal of Operational Research, 76(3), 486–500. https://doi.org/10.1016/0377-2217(94)90282-8
  • Mardani, A., Jusoh, A., & Zavadskas, E. K. (2015). Fuzzy multiple criteria decision-making techniques and applications - Two decades review from 1994 to 2014. Expert Systems with Applications, 42(8), 4126–4148. https://doi.org/10.1016/j.eswa.2015.01.003
  • Meng, K., Lou, P., Peng, X., & Prybutok, V. (2016). A hybrid approach for performance evaluation and optimized selection of recoverable end-of-life products in the reverse supply chain. Computers & Industrial Engineering, 98, 171–184. https://doi.org/10.1016/j.cie.2016.05.025
  • Özbek, A. (2014). Selection of Executives in Non-Governmental Organizations with an Integrated Approach. Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi, 6(2), 39–46. https://doi.org/10.29137/umagd.346092
  • Scott, J., Ho, W., Dey, P. K., & Talluri, S. (2015). A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments. International Journal of Production Economics, 166, 226–237. https://doi.org/10.1016/j.ijpe.2014.11.008
  • Serrai, W., Abdelli, A., Mokdad, L., & Hammal, Y. (2017). Towards an efficient and a more accurate web service selection using MCDM methods. Journal of Computational Science. https://doi.org/10.1016/j.jocs.2017.05.024
  • Uzun, S., & Kuşakcı, A. O. (2016). AFET LOJİSTİĞİ ALANINDA TESİS YERİ SEÇİMİ ÇALIŞMALARI. In International Symposium on Natural Hazards and Hazard Management 2016 (pp. 798–806). Karabük.
  • Yurdakul, M. (2004). AHP as a strategic decision-making tool to justify machine tool selection. Journal of Materials Processing Technology, 146(3), 365–376. https://doi.org/10.1016/j.jmatprotec.2003.11.026

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