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A Decision Support System for Product Selection Using Hybridized Fuzzy-AHP TOPSIS Methods

Year 2019, Volume: 11 Issue: 1, 99 - 108, 31.01.2019

Ali Osman Kusakci

https://doi.org/10.29137/umagd.370349
Cited By: 2

Abstract

Product selection
process requires perfect satisfaction of the customer needs and preferences in
terms of quality, cost and functionality. Considering this aspects, it is a
complex multi-criteria decision making problem. This statement is especially
true for such product families with wide product variety. This study aims to
design an interactive decison support tool for selecting industrial fans by
employing a hybridized fuzzy-AHP and TOPSIS approach. With this work, an expert
system for industrial fan selection is realized which collects customer’s
requirements and preferences with Fuzzy-AHP and ranks the best fitting
alternative products using TOPSIS approach.

Keywords

Multi-criteria desicion making Fuzzy-AHP TOPSIS Industrial Fan Selection

References

  • 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 Type-2 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

Ürün Seçimi için Hibritlenmiş Fuzzy-AHP ve TOPSIS Yöntemine Dayalı Bir Karar Destek Sistemi

Year 2019, Volume: 11 Issue: 1, 99 - 108, 31.01.2019

Ali Osman Kusakci

https://doi.org/10.29137/umagd.370349
Cited By: 2

Abstract

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 zahmetli bir Çok Kriterli Karar Verme (ÇKKV) problemidir. Bu
çalışmada, Bulanık-AHP ve TOPSIS metotlarını kullanarak endüstriyel tip fan
seçimi problemi için hibrit bir karar destek sistemi önerilmektedir. Önerilen
model ile müşterinin taleplerine ve önceliklerine göre kriter ağırlıklarının
Bulanık-AHP ile tesbiti yapılmaktadır. Elde edilen kriter ağırlıkları
kullanılarak TOPSIS yöntemi ile en iyi alternatifler sıralanmakta ve müşteriye
sunulmaktadır.

Keywords

Çok kriterli karar verme Fuzzy AHP TOPSIS Endüstriyel Fan Seçimi

References

  • 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 Type-2 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
There are 28 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Ali Osman Kusakci 0000-0003-1411-0369

Publication Date January 31, 2019
Submission Date December 23, 2017
Published in Issue Year 2019 Volume: 11 Issue: 1

Cite

APA Kusakci, A. O. (2019). Ürün Seçimi için Hibritlenmiş Fuzzy-AHP ve TOPSIS Yöntemine Dayalı Bir Karar Destek Sistemi. International Journal of Engineering Research and Development, 11(1), 99-108. https://doi.org/10.29137/umagd.370349

Cited By

Yeni ürün seçiminde çok kriterli karar verme ile simülasyonu birleştiren yaklaşım
Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi
https://doi.org/10.17341/gazimmfd.1183811
Genişletilmiş Analiz Yöntemine Dayalı Bulanık AHP ve TOPSIS Yöntemi ile Danışmanlık Sektöründe Sunulan Hizmetlerin Değerlendirilmesi
Akademik Araştırmalar ve Çalışmalar Dergisi (AKAD)
https://doi.org/10.20990/kilisiibfakademik.1231038

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