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Journals / Avrupa Bilim ve Teknoloji Dergisi / 2018 / Cilt: 0 - Sayı: 14

Evaluating R&D Projects Using Two Phases Fuzzy AHP and Fuzzy TOPSIS Methods

Onur CANPOLAT
Alper KİRAZ
Enes Furkan ERKAN
Fatih ALBAYRAK
Pages
49–53
Publication Date
2022-10-11
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In the model, the weights of the criteria are determined using the Fuzzy AHP (Analytical Hierarchy Process) method and themost appropriate project is determined by the Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions) method.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Technology provides important contributions to economic growth by increasing productivity in production.
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Key Findings & Data:One of the most importantindicators of technological innovation is research and development (R&D) activities.
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Conclusion & Impact:In the model, the weights of the criteria are determined using the Fuzzy AHP (Analytical Hierarchy Process) method and themost appropriate project is determined by the Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions) method.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Technology provides important contributions to economic growth by increasing productivity in production. One of the most importantindicators of technological innovation is research and development (R&D) activities. R&D studies have become necessary forcompanies to have a competitive advantage and to continue their operations more profitably. The decisions taken by companies and theinvestments they make have become more important than ever for their institutional future. In this sense, investments and projects inthe R&D have become a decisive factor in the future of companies, moving companies away from traditional financial approaches thatonly aim at cost or profit. Decisions to be made in this issue have a more complex structure than ever, and their effectiveness has becomecritical for corporations. In this study, a two-stage model is proposed for the decision of an R&D project selection decision of an energycompany. In the model, the weights of the criteria are determined using the Fuzzy AHP (Analytical Hierarchy Process) method and themost appropriate project is determined by the Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions) method.

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Cite This Article

CANPOLAT, O., KİRAZ, A., ERKAN, E. F., ALBAYRAK, F. (2022). Evaluating R&D Projects Using Two Phases Fuzzy AHP and Fuzzy TOPSIS Methods. Avrupa Bilim ve Teknoloji Dergisi, Cilt: 0 - Sayı: 14, 49–53. https://ajindex.com/articles/458309

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References (13)

  1. Amiri, M.P., 2010. Project selection for oil-fields development by using the AHP and fuzzy TOPSIS methods. Expert Syst. Appl. 37, 6218–6224. https://doi.org/10.1016/j.eswa.2010.02.103
  2. Bakshi, T., Sarkar, B., 2011. MCA Based Performance Evaluation of Project Selection. Int. J. Softw. Eng. Appl. 2, 14–22. https://doi.org/10.5121/ijsea.2011.2202
  3. Bakshi, T., Sinharay, A., Sarkar, B., 2011. Exploratory Analysis of Project Selection through MCDM 7. Bilici, M.S.U. 2002. Ülkemizin Teknolojik Gelişiminde ArGe'nin Önemi. TMMOB Madencilik Bülteni. Buckley, J.J. 1985. Fuzzy hierarchical analysis. Fuzzy Sets and Systems 34, 187-195.
  4. Chen, C.T. 2000. Extensions of the TOPSIS for group decisionmaking under fuzzy environment. Fuzzy Sets and Systems 114(1), 1-9.
  5. Değermenci, A., Ayvaz, B. 2016. Bulanık Ortamda Çok Kriterli Karar Verme Teknikleri ile Personel Seçimi: Katılım Bankacılığı Sektöründe Bir Uygulama. Journal of Science 15(30), 77-93.
  6. Ebrahimnejad, S., Mousavi, S.M., Tavakkoli-Moghaddam, R., Hashemi, H., Vahdani, B., 2012. A novel two-phase group decision making approach for construction project selection in a fuzzy environment. Appl. Math. Model. 36, 4197–4217. https://doi.org/10.1016/j.apm.2011.11.050
  7. Enea, M., Piazza, T., 2004. Project Selection by Constrained Fuzzy AHP. Fuzzy Optim. Decis. Mak. 3, 39–62. https://doi.org/10.1023/B:FODM.0000013071.63614.3d
  8. Erdem, M.B. 2016. A fuzzy analytical hierarchy process application on personnel selection in IT companies: A case study in a spin-off company. Acta Physica Polonica A 130(1), 331-334.
  9. Ghorabaee, M.K., Amiri, M., Sadaghiani, J.S., Zavadskas, E.K., 2015. Multi-Criteria Project Selection Using an Extended VIKOR Method with Interval Type-2 Fuzzy Sets. Int. J. Inf. Technol. Decis. Mak. 14, 993–1016. https://doi.org/10.1142/S0219622015500212
  10. Halouani, N., Chabchoub, H., Martel, J.-M., 2009. PROMETHEE-MD-2T method for project selection. Eur. J. Oper. Res. 195, 841–849. https://doi.org/10.1016/j.ejor.2007.11.016
  11. Huang, C.-C., Chu, P.-Y., Chiang, Y.-H., 2008. A fuzzy AHP application in government-sponsored R&D project selection. Omega, A Special Issue Dedicated to the 2008 Beijing Olympic Games 36, 1038–1052. https://doi.org/10.1016/j.omega.2006.05.003
  12. Liberatore, M.J., 1987. An extension of the analytic hierarchy process for industrial R&D project selection and resource allocation. IEEE Trans. Eng. Manag. EM-34, 12–18. https://doi.org/10.1109/TEM.1987.6498854
  13. Mahmoodzadeh, S., Shahrabi, J., Pariazar, M., Zaeri, M.S., 2