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BULANIK EDAS YÖNTEMİ İLE AR-GE PROJESİ SEÇİMİ

Year 2019, Issue: 24, 151 - 170, 24.07.2019
https://doi.org/10.18092/ulikidince.538332

Abstract

Günümüzde, Ar-Ge projesi seçimi
firmaların rekabet ortamında sürdürülebilir bir ilerleme sağlayabilmeleri için
oldukça önemlidir. Ar-Ge projesi seçim problemi bir çok nitel ve nicel kriter
altında çok sayıda proje alternatifi ile çok sayıda karar verici içerdiğinden
oldukça karmaşık bir problemdir. Çalışmanın amacı bu karmaşık probleme Çok
Kriterli Karar Verme (ÇKKV) yöntemlerinden biri olan EDAS yöntemi ile çözüm
getirmektir. Ayrıca problemin çok sayıda belirsizlik içermesi, alternatif ve
kriterlerin kesin ifadelerle değerlendirilmesinde zorluk yaşanması nedeniyle
önerilen yöntem bulanık mantık teorisi ile birlikte ele alınmıştır. Uygulamada
üç karar verici tarafından beş Ar-Ge proje alternatifi kriterler altında sözel
değişkenlerle değerlendirilerek en uygun proje belirlenmiştir.

References

  • Bard, J. Balachandra, R. Kaufmann. P. E. (1988). An Interactive Approach to R&D Project Selection and Termination. IEEE Transactions on Engineering Management , 35(3), 139 - 146.
  • Bayhan, H., G. (2018). Selection of Heating, Ventilating and Air Conditıoning (HVAC) Suppliers for Green Buildings with Fuzzy-Evaluation based on Distance from Average Solutıon (EDAS) Method (Yayımlanmış Yüksek Lisans Tezi). İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul.
  • Carlsson, C., Fullér, R., Heikkila, M., Majlender, P. (2007). A Fuzzy Approach to R&D Project Portfolio Selection. International Journal of Approximate Reasoning, 44, 93–105.
  • Eilat, H., Golany, B., Shtub, A. (2008). R&D Project Evaluation: An Integrated DEA and Balanced Scorecard Approach. Omega, 36, 895-912.
  • Keshavarz Ghorabaee, M., Zavadskas, E.K., Olfat, L., Turskis, Z. (2015). Multi-Criteria Inventory Classification Using a New Method of Evaluation Based on Distance From Average Solution (EDAS). Informatica, 26(3), 435–451.
  • Keshavarz Ghorabaee M., Zavadskas, E.K., Amiri, M., Turskis, Z. (2016). Extended EDAS Method for Fuzzy Multi-Criteria Decision-Making: An Application to Supplier Selection. Internatıonal Journal of Computers Communications & Control, 11(3), 358-371.
  • Keshavarz Ghorabaee M., Amiri, M., Zavadskas, E.K., Turskis, Z. (2017). Multi-Criteria Group Decision-Making Using an Extended EDAS Method with Interval Type-2 Fuzzy Sets. Economics and Management, 20, 48-68.
  • Keshavarz Ghorabaee M., Amiri, M., Zavadskas, E.K., Turskis, Z., Antucheviciene, J. (2018). A Dynamic Fuzzy Approach Based on the EDAS Method for Multi-Criteria Subcontractor Evaluation. Information, 9(3), 68.
  • Gültaş İ. (2007). Endüstri Mühendisliği Eğitiminde Matematik Ders İçeriklerinin Belirlenmesine Bulanık AHP Yöntemi ile Çözüm Önerisi (Yayımlanmış Yüksek Lisans Tezi). İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul.
  • Güryeli, M. (2016). Ar-Ge Projeleri Seçim Probleminin AHP Yöntemi ile İncelenmesi: Kamu Destekli Teknolojik Ürün Yatırım Destek Programı Üzerine Bir Uygulama. (Yayımlanmış Yüksek Lisans Tezi). Adnan Menderes Üniversitesi, Sosyal Bilimler Enstitüsü, Aydın.
  • Hall, D.L. , Nauda, A. (1988). Strategic Methodology for R&D Project Selection. Engineering Management Conference, 'Engineering Leadership in the 90's'.
  • Henriksen, A. D., Traynor, A. J., (1999). A Practical R&D Project-Selection Scoring Tool. IEEE Transactions on Engineering Management , 46(2),158 - 170.
  • Hsu, Y.G., Tzeng, G.H., Shyu, J.Z. (2003). Fuzzy Multiple Criteria Selection of Government‐Sponsored Frontier Technology R&D Projects. R&D Management, 33(5), 539-551.
  • Huang, C.C. Chu, P.Y. ve Chiang, Y.H. (2008). A Fuzzy AHP Application in Government Sponsored R&D Project Selection, Omega, 36, 1038-1052.
  • Karakaşoğlu, N. (2008). Bulanık Çok Kriterli Karar Verme Teknikleri ve Uygulama. (Yayımlanmış Yüksek Lisans Tezi). Pamukkale Üniversitesi, Sosyal Bilimler Enstitüsü, Denizli.
  • Kaya, İ., Oner, M. A., Başoğlu, N. (2003). Critical Success Factors in R&D Project Management in Military Systems Acquisition and a Suggested R&D Project Selection Methodology for Turkish Armed Forces. In PICMET Conference Proceedings.
  • Khorramshahgol R., Azani, H., Gousty, Y. (1988). Integrated Approach to Project Evaluation and Selection. IEEE Transactions on Engineering Management, 35(4),265 - 270.
  • Kuchta, D. (2001). A Fuzzy Model for R&D Project Selection with Benefit, Outcome and Resource Interactions. The Engineering Economist, 46(3), 164-180.
  • Kiraz, A., Canpolat, O., Erkan, E. F., Albayrak, F. (2018). Evaluating R&D Projects Using Two Phases Fuzzy AHP and Fuzzy TOPSIS Methods. Avrupa Bilim Ve Teknoloji Dergisi, 49-53.
  • Liang, W.Y. (2003) The Analytic Hierarchy Process in Project Evaluation: An R&D Case Study in Taiwan. Benchmarking: An International Journal, 10(5), 445-456.
  • Linton J. D., Morabito, J., Yeomans, J., S. (2007). An Extension to A DEA Support System Used for Assessing R&D Projects. R& D Management, 37(1), 29-36.
  • Meade, L.M., Presley, A. (2002). R&D Project Selection Using the Analytic Network Process. IEEE Transactions On Engineering Management , 49, 59-66.
  • Mohaghar, A., Fathi, M. R., Alireza Faghih, A., Turkayesh, M. M. (2012). An Integrated Approach of Fuzzy ANP And Fuzzy TOPSIS for R&D Project Selection: A Case Study. Australian Journal of Basic And Applied Sciences, 6(2), 66-75.
  • Mohanty, R. P., Agarwal, R., Choudhury, A. K., Tıwarı, M. K. (2005). A Fuzzy ANP-Based Approach To R&D Project Selection: A Case Study. International Journal of Production Research, 43, 5199–5216.
  • Peker, D. (2014). Ar-Ge Projelerinin Önceliklendirilmesi ve Seçimi için Çok Kriterli Bir Model Önerisi. (Yayımlanmış Yüksek Lisans Tezi). Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara.
  • Peng, x., Liu, .C. (2017). Algorithms for Neutrosophic Soft Decision Making Based on EDAS and New Similarity Measure. Journal Of Intelligent & Fuzzy Systems, 32(1), 955-968.
  • Poh, K. L., Ang, B.W., Bai, F. (2002). A Comparative Analysis of R&D Project Evaluation Methods. R& D Management, 31, 63-75.Ringuest, J. L., Graves, S., B. (1990). Linear R&D Project Selection Problem: An Alternative to Net Present Value. IEEE Transactions on Engineering Management, 37(2),143 - 146.
  • Sarı, E. B. (2017). Endüstri İşletmelerinde Ar-Ge Projelerini Öncelik Sıralamasında Entropi Ağırlıklı TOPSIS Yöntemine Dayalı Çok Kriterli Bir Analiz. International Journal of Academic Value Studies, 3(11), 159-170.
  • Stevic, Z., Vasiljevic, M., Zavadskas, E.K., Sremac, S., Turskis, Z. (2018). Selection of Carpenter Manufacturer Using Fuzzy EDAS Method. Inzinerine Ekonomika-Engineering Economcis, 29(3), 281-290.
  • Stanujkic, D., Zavadskas, E. K., Ghorabaee,M. K., Turskıs, Z. (2017). An Extension of The EDAS Method Based on the Use of Interval Grey Numbers. Studies in Informatics and Control, 26 (1), 5-12.
  • Tolga, Ç. (2008). Fuzzy Multicriteria R&D Project Selection with a Real Options Valuation Model. Journal of Intelligent and Fuzzy Systems, 19, 359-371.
  • Tolga, A.Ç., Kahraman, C. (2008). Fuzzy Multiattribute Evaluation of R&D Projects Using a Real Options Valuation Model. International Journal of Intelligent Systems, 23, 1153-1176.
  • Yakıcı Ayan T., Perçin S. ( 2012). Ar-Ge Projelerinin Seçiminde Grup Kararına Dayalı Bulanık Karar Verme Yaklaşımı. Atatürk Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 26 (2).
  • TÜBİTAK 1505 Üniversite-Sanayi İşbirliği Destek Programı Proje Öneri Değerlendirme Raporu Agy205-02. Ankara. Erişim Adresi http://www.tubitak.gov.tr/sites/default/files/agy205_060613.pdf.
  • TÜBİTAK (2012). 1501 Sanayi Ar-Ge Projeleri Destekleme Programı Proje Öneri Değerlendirme Raporu (Agy200) Hazırlama Kılavuzu. Erişim Adresi http://bap.beun.edu.tr/Dosyalar/F16046.pdf.
  • Tuzkaya,U. R. Yolver, E. (2015 ). R&D Project Selection by Integrated Grey Analytic Network Process and Grey Relational Analysis: An Implementatıon for Home Appliances Company. Journal of Aeronautics and Space Technologies, 8, 35-41.
  • Yıldız, A. (2014). Bulanık VIKOR Yöntemini Kullanarak Proje Seçim Sürecinin İncelenmesi. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 115-128.
  • Wang, J., Hwang, W.-L. (2007). A Fuzzy Set Approach For R&D Portfolio Selection Using a Real Options Valuation Model. Omega, 35, 247-257.
  • Wang, Y.M., Yang, J.B., Xu, D.L., Chin, K.S. (2006) On the centroids of fuzzy numbers. Fuzzy Sets Syst. 157,919–926.
  • Wang, K., Wang, C.K., Hu, C. (2005). Analytic Hierarchy Process with Fuzzy Scoring In Evaluating Multidisciplinary R&D Projects In China. IEEE Transactions On Engineering Management , 52, 119 - 129.
  • Zadeh L.A. (1965). Fuzzy sets. Information and Control, 8, 338-353.

R&D PROJECT SELECTION WITH FUZZY EDAS METHOD

Year 2019, Issue: 24, 151 - 170, 24.07.2019
https://doi.org/10.18092/ulikidince.538332

Abstract

Nowadays, R & D project
selection is very important for companies to achieve sustainable progress in
the competitive environment. As R & D project selection problem contains a
large number of project alternatives and many decision makers under many
qualitative and quantitative criteria, it is a highly complex problem. The aim
of this study is to solve this complex problem with the EDAS method which is
one of the Multi Criteria Decision Making (MCDM) methods. In addition, since
the problem has many uncertainties and the difficulty in evaluating
alternatives and criteria with definite expressions, the proposed method is
discussed together with fuzzy logic
theory.
In practice, five R & D project alternatives were evaluated with linguistic
variables by three decision makers and the most suitable project was determined.

References

  • Bard, J. Balachandra, R. Kaufmann. P. E. (1988). An Interactive Approach to R&D Project Selection and Termination. IEEE Transactions on Engineering Management , 35(3), 139 - 146.
  • Bayhan, H., G. (2018). Selection of Heating, Ventilating and Air Conditıoning (HVAC) Suppliers for Green Buildings with Fuzzy-Evaluation based on Distance from Average Solutıon (EDAS) Method (Yayımlanmış Yüksek Lisans Tezi). İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul.
  • Carlsson, C., Fullér, R., Heikkila, M., Majlender, P. (2007). A Fuzzy Approach to R&D Project Portfolio Selection. International Journal of Approximate Reasoning, 44, 93–105.
  • Eilat, H., Golany, B., Shtub, A. (2008). R&D Project Evaluation: An Integrated DEA and Balanced Scorecard Approach. Omega, 36, 895-912.
  • Keshavarz Ghorabaee, M., Zavadskas, E.K., Olfat, L., Turskis, Z. (2015). Multi-Criteria Inventory Classification Using a New Method of Evaluation Based on Distance From Average Solution (EDAS). Informatica, 26(3), 435–451.
  • Keshavarz Ghorabaee M., Zavadskas, E.K., Amiri, M., Turskis, Z. (2016). Extended EDAS Method for Fuzzy Multi-Criteria Decision-Making: An Application to Supplier Selection. Internatıonal Journal of Computers Communications & Control, 11(3), 358-371.
  • Keshavarz Ghorabaee M., Amiri, M., Zavadskas, E.K., Turskis, Z. (2017). Multi-Criteria Group Decision-Making Using an Extended EDAS Method with Interval Type-2 Fuzzy Sets. Economics and Management, 20, 48-68.
  • Keshavarz Ghorabaee M., Amiri, M., Zavadskas, E.K., Turskis, Z., Antucheviciene, J. (2018). A Dynamic Fuzzy Approach Based on the EDAS Method for Multi-Criteria Subcontractor Evaluation. Information, 9(3), 68.
  • Gültaş İ. (2007). Endüstri Mühendisliği Eğitiminde Matematik Ders İçeriklerinin Belirlenmesine Bulanık AHP Yöntemi ile Çözüm Önerisi (Yayımlanmış Yüksek Lisans Tezi). İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul.
  • Güryeli, M. (2016). Ar-Ge Projeleri Seçim Probleminin AHP Yöntemi ile İncelenmesi: Kamu Destekli Teknolojik Ürün Yatırım Destek Programı Üzerine Bir Uygulama. (Yayımlanmış Yüksek Lisans Tezi). Adnan Menderes Üniversitesi, Sosyal Bilimler Enstitüsü, Aydın.
  • Hall, D.L. , Nauda, A. (1988). Strategic Methodology for R&D Project Selection. Engineering Management Conference, 'Engineering Leadership in the 90's'.
  • Henriksen, A. D., Traynor, A. J., (1999). A Practical R&D Project-Selection Scoring Tool. IEEE Transactions on Engineering Management , 46(2),158 - 170.
  • Hsu, Y.G., Tzeng, G.H., Shyu, J.Z. (2003). Fuzzy Multiple Criteria Selection of Government‐Sponsored Frontier Technology R&D Projects. R&D Management, 33(5), 539-551.
  • Huang, C.C. Chu, P.Y. ve Chiang, Y.H. (2008). A Fuzzy AHP Application in Government Sponsored R&D Project Selection, Omega, 36, 1038-1052.
  • Karakaşoğlu, N. (2008). Bulanık Çok Kriterli Karar Verme Teknikleri ve Uygulama. (Yayımlanmış Yüksek Lisans Tezi). Pamukkale Üniversitesi, Sosyal Bilimler Enstitüsü, Denizli.
  • Kaya, İ., Oner, M. A., Başoğlu, N. (2003). Critical Success Factors in R&D Project Management in Military Systems Acquisition and a Suggested R&D Project Selection Methodology for Turkish Armed Forces. In PICMET Conference Proceedings.
  • Khorramshahgol R., Azani, H., Gousty, Y. (1988). Integrated Approach to Project Evaluation and Selection. IEEE Transactions on Engineering Management, 35(4),265 - 270.
  • Kuchta, D. (2001). A Fuzzy Model for R&D Project Selection with Benefit, Outcome and Resource Interactions. The Engineering Economist, 46(3), 164-180.
  • Kiraz, A., Canpolat, O., Erkan, E. F., Albayrak, F. (2018). Evaluating R&D Projects Using Two Phases Fuzzy AHP and Fuzzy TOPSIS Methods. Avrupa Bilim Ve Teknoloji Dergisi, 49-53.
  • Liang, W.Y. (2003) The Analytic Hierarchy Process in Project Evaluation: An R&D Case Study in Taiwan. Benchmarking: An International Journal, 10(5), 445-456.
  • Linton J. D., Morabito, J., Yeomans, J., S. (2007). An Extension to A DEA Support System Used for Assessing R&D Projects. R& D Management, 37(1), 29-36.
  • Meade, L.M., Presley, A. (2002). R&D Project Selection Using the Analytic Network Process. IEEE Transactions On Engineering Management , 49, 59-66.
  • Mohaghar, A., Fathi, M. R., Alireza Faghih, A., Turkayesh, M. M. (2012). An Integrated Approach of Fuzzy ANP And Fuzzy TOPSIS for R&D Project Selection: A Case Study. Australian Journal of Basic And Applied Sciences, 6(2), 66-75.
  • Mohanty, R. P., Agarwal, R., Choudhury, A. K., Tıwarı, M. K. (2005). A Fuzzy ANP-Based Approach To R&D Project Selection: A Case Study. International Journal of Production Research, 43, 5199–5216.
  • Peker, D. (2014). Ar-Ge Projelerinin Önceliklendirilmesi ve Seçimi için Çok Kriterli Bir Model Önerisi. (Yayımlanmış Yüksek Lisans Tezi). Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara.
  • Peng, x., Liu, .C. (2017). Algorithms for Neutrosophic Soft Decision Making Based on EDAS and New Similarity Measure. Journal Of Intelligent & Fuzzy Systems, 32(1), 955-968.
  • Poh, K. L., Ang, B.W., Bai, F. (2002). A Comparative Analysis of R&D Project Evaluation Methods. R& D Management, 31, 63-75.Ringuest, J. L., Graves, S., B. (1990). Linear R&D Project Selection Problem: An Alternative to Net Present Value. IEEE Transactions on Engineering Management, 37(2),143 - 146.
  • Sarı, E. B. (2017). Endüstri İşletmelerinde Ar-Ge Projelerini Öncelik Sıralamasında Entropi Ağırlıklı TOPSIS Yöntemine Dayalı Çok Kriterli Bir Analiz. International Journal of Academic Value Studies, 3(11), 159-170.
  • Stevic, Z., Vasiljevic, M., Zavadskas, E.K., Sremac, S., Turskis, Z. (2018). Selection of Carpenter Manufacturer Using Fuzzy EDAS Method. Inzinerine Ekonomika-Engineering Economcis, 29(3), 281-290.
  • Stanujkic, D., Zavadskas, E. K., Ghorabaee,M. K., Turskıs, Z. (2017). An Extension of The EDAS Method Based on the Use of Interval Grey Numbers. Studies in Informatics and Control, 26 (1), 5-12.
  • Tolga, Ç. (2008). Fuzzy Multicriteria R&D Project Selection with a Real Options Valuation Model. Journal of Intelligent and Fuzzy Systems, 19, 359-371.
  • Tolga, A.Ç., Kahraman, C. (2008). Fuzzy Multiattribute Evaluation of R&D Projects Using a Real Options Valuation Model. International Journal of Intelligent Systems, 23, 1153-1176.
  • Yakıcı Ayan T., Perçin S. ( 2012). Ar-Ge Projelerinin Seçiminde Grup Kararına Dayalı Bulanık Karar Verme Yaklaşımı. Atatürk Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 26 (2).
  • TÜBİTAK 1505 Üniversite-Sanayi İşbirliği Destek Programı Proje Öneri Değerlendirme Raporu Agy205-02. Ankara. Erişim Adresi http://www.tubitak.gov.tr/sites/default/files/agy205_060613.pdf.
  • TÜBİTAK (2012). 1501 Sanayi Ar-Ge Projeleri Destekleme Programı Proje Öneri Değerlendirme Raporu (Agy200) Hazırlama Kılavuzu. Erişim Adresi http://bap.beun.edu.tr/Dosyalar/F16046.pdf.
  • Tuzkaya,U. R. Yolver, E. (2015 ). R&D Project Selection by Integrated Grey Analytic Network Process and Grey Relational Analysis: An Implementatıon for Home Appliances Company. Journal of Aeronautics and Space Technologies, 8, 35-41.
  • Yıldız, A. (2014). Bulanık VIKOR Yöntemini Kullanarak Proje Seçim Sürecinin İncelenmesi. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 115-128.
  • Wang, J., Hwang, W.-L. (2007). A Fuzzy Set Approach For R&D Portfolio Selection Using a Real Options Valuation Model. Omega, 35, 247-257.
  • Wang, Y.M., Yang, J.B., Xu, D.L., Chin, K.S. (2006) On the centroids of fuzzy numbers. Fuzzy Sets Syst. 157,919–926.
  • Wang, K., Wang, C.K., Hu, C. (2005). Analytic Hierarchy Process with Fuzzy Scoring In Evaluating Multidisciplinary R&D Projects In China. IEEE Transactions On Engineering Management , 52, 119 - 129.
  • Zadeh L.A. (1965). Fuzzy sets. Information and Control, 8, 338-353.
There are 41 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Figen Kas Bayrakdaroğlu This is me 0000-0003-0715-8049

Nilsen Kundakcı 0000-0002-7283-320X

Publication Date July 24, 2019
Published in Issue Year 2019 Issue: 24

Cite

APA Kas Bayrakdaroğlu, F., & Kundakcı, N. (2019). BULANIK EDAS YÖNTEMİ İLE AR-GE PROJESİ SEÇİMİ. Uluslararası İktisadi Ve İdari İncelemeler Dergisi(24), 151-170. https://doi.org/10.18092/ulikidince.538332

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