Araştırma Makalesi
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Teknoloji Geliştirme Bölgelerinde Ar-Ge Faaliyetinde Bulunan Firmaların Sıralama Problemine Bulanık Hibrit Bir Yaklaşım

Yıl 2023, Cilt: 6 Sayı: 2, 1452 - 1468, 05.07.2023
https://doi.org/10.47495/okufbed.1171050

Öz

Teknoloji geliştirme bölgeleri üniversitelerin sanayi işbirlikleri ve kurumlarıyla bilgi paylaştığı ve alanında geniş bir şirket yelpazesine sahip olan bölgelerdir. Gelişen teknoloji ve rekabet, Araştırma ve Geliştirme (Ar-Ge) faaliyetleri yürüten teknoloji geliştirme bölgelerindeki firmaları yenilik yapmaya zorlamış ve böylece bu firmalar sanayi kuruluşlarına farklı projeler önererek ve yenilikler yaparak kendilerini kanıtlamaya çalışmışlardır ve hükümet tarafından bazı promosyonlar alabilecekleri için hala da çalışmaktadırlar. Şirketlerin sıralanmasına ilişkin yöntem, finansal teşvikler ve altyapıdan Ar-Ge faaliyetlerine kadar bir dizi kritere sahiptir. Kriterlerin çoğu, karar vericilerin bulanık sayılarla ifade edebileceği dilsel terimlere dayanmaktadır. Bu nedenle bu çalışmada, Bulanık Analitik Hiyerarşi Süreci ve Bulanık TOPSIS yöntemlerinin birleştirilmesiyle, Ar-Ge faaliyetleri yürüten firmaların sıralanması problemi hibrit bir model kullanılarak çözülmüştür. Bu çalışma için dört farklı ana karar kriteri, 16 alt kriter ve üç rastgele şirket dikkate alınmıştır. Türkiye'nin bazı teknoloji geliştirme bölgelerinden uzmanlar dahil olmak üzere Ar-Ge faaliyetleri konusunda uzmanların görüşleri alınarak bir ağ oluşturulmuş ve anketler yapılmıştır. Önerilen hibrit model, Türkiye'de Çukurova Üniversitesi Teknoloji Geliştirme Bölgesi'nde Ar-Ge faaliyetleri yürüten şirketlerin sıralaması için bir vaka çalışmasına başarıyla uygulanmıştır. Gerçek veriler kullanılarak, önerilen yaklaşımın uygulanabilirliği gösterilmiş ve önerilen yöntemle elde edilen en iyi alternatif Türkiye'deki Çukurova Üniversitesi Teknoloji Geliştirme Bölgesi'ndeki üç şirket için karşılaştırılmıştır.

Kaynakça

  • Baykasoğlu A., Kaplanoğlu V., Durmuşoğlu Z.D.U., Şahin C.Integrating fuzzy DEMATEL and fuzzy hierarchical TOPSIS methods for truck selection. Expert Systems with Applications 2013; 40(3):899-907.
  • Biswas P., Pramanik S., Giri B.C. TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Computing and Applications 2016; 27:727-737.
  • Buckley J.J. Fuzzy hierarchical analysis. Fuzzy Set Syst 1985; 17 (3):233-247.
  • Chang D.Y. Applications of the extent analysis method on fuzzy AHP. European journal of operational research 1996; 95(3):649-655.
  • Chu T.C. Selecting plant location via a fuzzy TOPSIS approach. The International Journal of Advanced Manufacturing Technology 2002; 20 (11):859-864.
  • Das P. In search of best alternatives: a TOPSIS driven MCDM procedure for neural network modeling. Neural Computing and Applications 2010; 19 (1):91-102.
  • Dincer H. HHI-based evaluation of the European banking sector using an integrated fuzzy approach. Kybernetes 2019; 48:1195-1215.
  • Gopal J., Sangaiah A.K., Basu A., Gao X.Z. Integration of fuzzy DEMATEL and FMCDM approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome. International Journal of Machine Learning and Cybernetics 2018; 9: 225-241.
  • Hwang C.L., Yoon K. Multiple Attribute Decision Making, Methods and Applications: a State of the Art Survey Lecture notes in economics and mathematical systems. 1981; Springer-Verlag, New York, NY.
  • Hwang C.L., Yoon K. Multiple Attributes Decision Making Methods and Applications. 1981; Springer, Berlin Heidelberg.
  • Khorsheed M.S., Al-Fawzan M.A. Fostering university–industry collaboration in Saudi Arabia through technology innovation centers. Innovation 2014; 16(2):224-237.
  • Kulikova N.N., Kolomyts O.N., Litvinenko I.L., Gurieva L.K., Kamberdiyeva S.S. Features of formation and development of innovation centers generate. International Journal of Economics and Financial Issues 2016; 6(1S):74-80.
  • Luna M., Llorente I., Cobo A. A fuzzy approach to decision‐making in sea‐cage aquaculture production. International Transactions in Operational Research 2020; 1-25.
  • Mardani A., Jusoh A., Zavadskas E.K. Fuzzy multiple criteria decision-making techniques and applications–Two decades review from 1994 to 2014. Expert Systems with Applications 2015; 42(8):4126-4148.
  • Mashal I., Alsaryrah O. Fuzzy analytic hierarchy process model for multi-criteria analysis of internet of things. Kybernetes 2019; 2509-2520.
  • Özkan B., Özceylan E., Çetinkaya C. A GIS-based DANP-VIKOR approach to evaluate R&D performance of Turkish cities. Kybernetes 2019; 48: 2266-2306.
  • Saaty T.L. Hierarchical-multiobjective systems. Control-Theory and Advanced Technology 1989; 5(4):485-489.
  • Saaty T.L., Ozdemir M.S.Why the magic number seven plus or minus two. Mathematical and Computer Modelling 2003; 38 (3):233-244.
  • Sangaiah A., Thangavelu A. An exploration of FMCDM approach for evaluating the outcome/success of GSD projects. Open Engineering formerly Central European Journal of Engineering 2013; 3 (3): 419-435.
  • Sangaiah A.K., Gopal J., Basu A., Subramaniam P.R. An integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome. Neural Computing and Applications 2017; 28:111-123.
  • Sangaiah A.K., Subramaniam P.R., Zheng X. A combined fuzzy DEMATEL and fuzzy TOPSIS approach for evaluating GSD project outcome factors. Neural Computing and Applications 2015; 26 (5), 1025-1040.
  • Yu C., Zou Z., Shao Y., Zhang F. An integrated supplier selection approach incorporating decision maker’s risk attitude using ANN, AHP and TOPSIS methods. Kybernetes 2019; 49:2263-2284.
  • Zadeh L.A. Fuzzy Sets. Information and Control 1965; 8:338-353.

A Hybrid Fuzzy Model To The Ranking Problem of Companies Operating R&D Activities in Technology Innovation Centers

Yıl 2023, Cilt: 6 Sayı: 2, 1452 - 1468, 05.07.2023
https://doi.org/10.47495/okufbed.1171050

Öz

Technology innovation centers where universities share information with industry collaborations and institutions and have a wide range of companies in its field. Developing technology and competition have forced to make innovations, the companies in technology innovation centers operating Research and Development (R&D) activities have tried to prove themselves by proposing different projects to industrial organizations and making innovations and they are still working since they may get some promotions from the government. A decision for ranking companies has a number of criteria, which range from financial incentives and infrastructure to R&D activities. Most of the criteria are based on linguistic terms that decision makers can express with fuzzy statements. For this reason, in this study, the problem for ranking the companies operating R&D activities is solved by using a hybrid model, combining the Fuzzy Analytic Hierarchy Process and Fuzzy TOPSIS methods. Four different main decision criteria, 16 sub-criteria and three random companies were considered for this study. A network was formed, and surveys were carried out with the opinions of the experts on R&D activities and including experts from some technology development zones of Turkey. The proposed hybrid model was applied successfully to a case study for the ranking the companies operating R&D activities in Çukurova University Technology Development Zone in Turkey. Using actual data, it is showed the applicability of the proposed approach and compare the best alternative obtained by the proposed method for three companies in Çukurova University Technology Development Zone in Turkey.

Kaynakça

  • Baykasoğlu A., Kaplanoğlu V., Durmuşoğlu Z.D.U., Şahin C.Integrating fuzzy DEMATEL and fuzzy hierarchical TOPSIS methods for truck selection. Expert Systems with Applications 2013; 40(3):899-907.
  • Biswas P., Pramanik S., Giri B.C. TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Computing and Applications 2016; 27:727-737.
  • Buckley J.J. Fuzzy hierarchical analysis. Fuzzy Set Syst 1985; 17 (3):233-247.
  • Chang D.Y. Applications of the extent analysis method on fuzzy AHP. European journal of operational research 1996; 95(3):649-655.
  • Chu T.C. Selecting plant location via a fuzzy TOPSIS approach. The International Journal of Advanced Manufacturing Technology 2002; 20 (11):859-864.
  • Das P. In search of best alternatives: a TOPSIS driven MCDM procedure for neural network modeling. Neural Computing and Applications 2010; 19 (1):91-102.
  • Dincer H. HHI-based evaluation of the European banking sector using an integrated fuzzy approach. Kybernetes 2019; 48:1195-1215.
  • Gopal J., Sangaiah A.K., Basu A., Gao X.Z. Integration of fuzzy DEMATEL and FMCDM approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome. International Journal of Machine Learning and Cybernetics 2018; 9: 225-241.
  • Hwang C.L., Yoon K. Multiple Attribute Decision Making, Methods and Applications: a State of the Art Survey Lecture notes in economics and mathematical systems. 1981; Springer-Verlag, New York, NY.
  • Hwang C.L., Yoon K. Multiple Attributes Decision Making Methods and Applications. 1981; Springer, Berlin Heidelberg.
  • Khorsheed M.S., Al-Fawzan M.A. Fostering university–industry collaboration in Saudi Arabia through technology innovation centers. Innovation 2014; 16(2):224-237.
  • Kulikova N.N., Kolomyts O.N., Litvinenko I.L., Gurieva L.K., Kamberdiyeva S.S. Features of formation and development of innovation centers generate. International Journal of Economics and Financial Issues 2016; 6(1S):74-80.
  • Luna M., Llorente I., Cobo A. A fuzzy approach to decision‐making in sea‐cage aquaculture production. International Transactions in Operational Research 2020; 1-25.
  • Mardani A., Jusoh A., Zavadskas E.K. Fuzzy multiple criteria decision-making techniques and applications–Two decades review from 1994 to 2014. Expert Systems with Applications 2015; 42(8):4126-4148.
  • Mashal I., Alsaryrah O. Fuzzy analytic hierarchy process model for multi-criteria analysis of internet of things. Kybernetes 2019; 2509-2520.
  • Özkan B., Özceylan E., Çetinkaya C. A GIS-based DANP-VIKOR approach to evaluate R&D performance of Turkish cities. Kybernetes 2019; 48: 2266-2306.
  • Saaty T.L. Hierarchical-multiobjective systems. Control-Theory and Advanced Technology 1989; 5(4):485-489.
  • Saaty T.L., Ozdemir M.S.Why the magic number seven plus or minus two. Mathematical and Computer Modelling 2003; 38 (3):233-244.
  • Sangaiah A., Thangavelu A. An exploration of FMCDM approach for evaluating the outcome/success of GSD projects. Open Engineering formerly Central European Journal of Engineering 2013; 3 (3): 419-435.
  • Sangaiah A.K., Gopal J., Basu A., Subramaniam P.R. An integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome. Neural Computing and Applications 2017; 28:111-123.
  • Sangaiah A.K., Subramaniam P.R., Zheng X. A combined fuzzy DEMATEL and fuzzy TOPSIS approach for evaluating GSD project outcome factors. Neural Computing and Applications 2015; 26 (5), 1025-1040.
  • Yu C., Zou Z., Shao Y., Zhang F. An integrated supplier selection approach incorporating decision maker’s risk attitude using ANN, AHP and TOPSIS methods. Kybernetes 2019; 49:2263-2284.
  • Zadeh L.A. Fuzzy Sets. Information and Control 1965; 8:338-353.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Endüstri Mühendisliği
Bölüm Araştırma Makaleleri (RESEARCH ARTICLES)
Yazarlar

Kübra Tümay Ateş 0000-0002-3337-7969

Cenk Şahin 0000-0002-6076-7794

Yayımlanma Tarihi 5 Temmuz 2023
Gönderilme Tarihi 10 Eylül 2022
Kabul Tarihi 13 Şubat 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 6 Sayı: 2

Kaynak Göster

APA Tümay Ateş, K., & Şahin, C. (2023). A Hybrid Fuzzy Model To The Ranking Problem of Companies Operating R&D Activities in Technology Innovation Centers. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(2), 1452-1468. https://doi.org/10.47495/okufbed.1171050
AMA Tümay Ateş K, Şahin C. A Hybrid Fuzzy Model To The Ranking Problem of Companies Operating R&D Activities in Technology Innovation Centers. OKÜ Fen Bil. Ens. Dergisi ((OKU Journal of Nat. & App. Sci). Temmuz 2023;6(2):1452-1468. doi:10.47495/okufbed.1171050
Chicago Tümay Ateş, Kübra, ve Cenk Şahin. “A Hybrid Fuzzy Model To The Ranking Problem of Companies Operating R&D Activities in Technology Innovation Centers”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 6, sy. 2 (Temmuz 2023): 1452-68. https://doi.org/10.47495/okufbed.1171050.
EndNote Tümay Ateş K, Şahin C (01 Temmuz 2023) A Hybrid Fuzzy Model To The Ranking Problem of Companies Operating R&D Activities in Technology Innovation Centers. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 6 2 1452–1468.
IEEE K. Tümay Ateş ve C. Şahin, “A Hybrid Fuzzy Model To The Ranking Problem of Companies Operating R&D Activities in Technology Innovation Centers”, OKÜ Fen Bil. Ens. Dergisi ((OKU Journal of Nat. & App. Sci), c. 6, sy. 2, ss. 1452–1468, 2023, doi: 10.47495/okufbed.1171050.
ISNAD Tümay Ateş, Kübra - Şahin, Cenk. “A Hybrid Fuzzy Model To The Ranking Problem of Companies Operating R&D Activities in Technology Innovation Centers”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 6/2 (Temmuz 2023), 1452-1468. https://doi.org/10.47495/okufbed.1171050.
JAMA Tümay Ateş K, Şahin C. A Hybrid Fuzzy Model To The Ranking Problem of Companies Operating R&D Activities in Technology Innovation Centers. OKÜ Fen Bil. Ens. Dergisi ((OKU Journal of Nat. & App. Sci). 2023;6:1452–1468.
MLA Tümay Ateş, Kübra ve Cenk Şahin. “A Hybrid Fuzzy Model To The Ranking Problem of Companies Operating R&D Activities in Technology Innovation Centers”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 6, sy. 2, 2023, ss. 1452-68, doi:10.47495/okufbed.1171050.
Vancouver Tümay Ateş K, Şahin C. A Hybrid Fuzzy Model To The Ranking Problem of Companies Operating R&D Activities in Technology Innovation Centers. OKÜ Fen Bil. Ens. Dergisi ((OKU Journal of Nat. & App. Sci). 2023;6(2):1452-68.

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