Araştırma Makalesi
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Intuitionistic fuzzy multi-expert & multi-criteria decision making methodology: an application in healthcare industry

Yıl 2022, Cilt: 37 Sayı: 2, 1047 - 1062, 28.02.2022
https://doi.org/10.17341/gazimmfd.833468

Öz

Fuzzy set extensions have been one of the uncertainty handling tools used in many studies in recent years. However, it is a fact that the relations and transformations between these extensions have not been discussed sufficiently in the literature. Since the 1990s, fuzzy decision making theory has been one of the most studied topics in the literature. Specifically in recent years, many fuzzy decision making models have been proposed by using new fuzzy set extensions. Multi-criteria decision making models have been a research area where these extensions are frequently used. In this study, an integrated AHP-TOPSIS methodology employing triangular intuitionistic fuzzy numbers has been transformed into a methodology in which interval-valued triangular fuzzy numbers can be used. As an application, a surgery robot evaluation and selection problem for a newly founded private hospital to be a part of a hospital chain in Istanbul, has been analyzed. The obtained results showed that different fuzzy set extension based datasets can be successfully used in the same methodology by converting them to each other. In this study, comparison and sensitivity analyses are also performed and it has been observed that the results of the proposed model are quite robust and consistent.

Kaynakça

  • Zadeh, L.A., Fuzzy Sets, Inf. Control, 8, 338-353, 1965.
  • Zadeh, L.A., The Concept of a Linguistic Variable and its Application to Approximate Reasoning, Inf. Sci., 8, 199-249, 1975.
  • Sadiq, R., Tesfamariam, S., Environmental Decision-Making Under Uncertainty Using Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP), Stochastic Environ. Res. Risk Assess., 23, 75-91, 2009.
  • Kavita Yadav, S.P. ve Kumar, S., A Multi-criteria Interval-Valued Intuitionistic Fuzzy Group Decision Making for Supplier Selection with TOPSIS Method, RSFDGrC 2009: Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, 5908, Editör: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W., Springer, Berlin, Almanya, 2009.
  • Dammak, F., Baccour, L., Alimi, A.M., Interval Valued Intuitionistic Fuzzy Weight Techniques for TOPSIS Method, 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), Agadir - Morocco, 29 Kasım - 2 Aralık, 2016.
  • Karasan, A., Erdogan, M., Ilbahar, E., Prioritization of Production Strategies of a Manufacturing Plant by Using an Integrated Intuitionistic Fuzzy AHP & TOPSIS Approach, J. Enterp. Inf. Manage., 31 (4), 510-528, 2018.
  • Tooranloo, H.S., Ayatollah, A.S., Iranpour, A., A Model for Supplier Evaluation and Selection Based on Integrated Interval-valued Intuitionistic Fuzzy AHP-TOPSIS Approach, Int. J. Math. Oper. Res., 13 (3), 401-417, 2018.
  • Mousakhani, S., Nazari-Shirkouhi, S., Bozorgi-Amiri, A., A Novel Interval Type-2 Fuzzy Evaluation Model Based Group Decision Analysis for Green Supplier Selection Problems: a Case Study of Battery Industry, J. Cleaner Prod., 168, 205-218, 2017.
  • Toklu, M.C. Interval Type-2 Fuzzy TOPSIS Method for Calibration Supplier Selection Problem: a Case Study in an Automotive Company, Arabian J. Geosci., 11, 341, 2018.
  • Kahraman, C., Öztayşi, B., Cevik Onar, S., Doğan, O., Intuitionistic Fuzzy Originated Interval Type-2 Fuzzy AHP: an Application to Damless Hydroelectric Power Plants, Int. J. Anal. Hierarchy Process, 10(2), 226-292, 2018a.
  • Mei, Y., Xie, K., An Improved TOPSIS Method for Metro Station Evacuation Strategy Selection in Interval Type-2 Fuzzy Environment, Cluster Comput., 22, 2781-2792, 2019.
  • Prabhu, M., Nawzad Abdullah, N., Rzgar Ahmed, R., Nambirajan, T., Pandiyan, S., Segmenting the Manufacturing Industries and Measuring the Performance: Using Interval-valued Triangular Fuzzy TOPSIS Method, Complex Intell. Syst., 6, 591-606, 2020.
  • Sahin, A., Pehlivan, N.Y., Evaluation of Life Quality by Integrated Method of AHP and TOPSIS Based On Interval Type-2 Fuzzy Sets, Hacettepe J. Math. Stat., 46 (3), 511-523, 2017.
  • Wu, Y., Xu, C., Zhang, B., Tao, Y. , Li, X., Chu, H., Liu, F., Sustainability Performance Assessment of Wind Power Coupling Hydrogen Storage Projects Using a Hybrid Evaluation Technique Based on Interval Type-2 Fuzzy Set, Energy, 179, 1176-1190, 2019.
  • Mathew, M., Chakrabortty, R.K. and Ryan, M.J., Selection of an Optimal Maintenance Strategy Under Uncertain Conditions: An Interval Type-2 Fuzzy AHP-TOPSIS Method, IEEE Trans. Eng. Manage., 1-14, 2020.
  • Mardani, A., Hooker, R.E., Ozkul, S., Yifan, S., Nilashi, M., Sabzi, H.Z., Fei, G.C., Application of Decision Making and Fuzzy Sets Theory to Evaluate the Healthcare and Medical Problems: a Review of Three Decades of Research with Recent Developments, Expert Syst. Appl., 137, 202-231, 2019.
  • Kulak, O., Goren, H.G., Supciller, A.A., A New Multi Criteria Decision Making Approach for Medical Imaging Systems Considering Risk Factors, Appl. Soft Comput., 35, 931-941, 2015.
  • Afful-Dadzie, E., Nabareseh, S., Komínková Oplatková, Z., Klímek, P., Model for Assessing Quality of Online Health Information: a Fuzzy VIKOR Based Method, J. Multi-Criteria Decis. Anal., 23, 49-62, 2016.
  • Zhou, F., Wang, X., Goh, M., Fuzzy Extended VIKOR-based Mobile Robot Selection Model for Hospital Pharmacy, Int. J. Adv. Rob. Syst., 1-11, 2018.
  • Abdel-Basset, M., Manogaran, G., Gamal, A., Smarandache, F., A Group Decision Making Framework Based on Neutrosophic TOPSIS Approach for Smart Medical Device Selection, J. Med. Syst., 43, 38, 2019.
  • Büyüközkan, G., Göçer, F., Smart Medical Device Selection Based on Intuitionistic Fuzzy Choquet Integral, Soft Comput., 23, 10085-10103, 2019.
  • Büyüközkan, G., Mukul, E., Evaluation of Smart Health Technologies with Hesitant Fuzzy Linguistic MCDM Methods, J. Intell. Fuzzy Syst., 1-13, 2020.
  • Zadeh, L.A., Is There a Need for Fuzzy Logic?, Inf. Sci., 178 (13), 2751-2779, 2008.
  • Kutlu Gündoğdu, F., Kahraman, C., Spherical Fuzzy Sets and Spherical Fuzzy TOPSIS Method, J. Intell. Fuzzy Syst., 36 (1), 337-352, 2019.
  • Sambuc, R., Function Φ-Flous. Application a l’aide au Diagnostic en Pathologie Thyroidienne, These de Doctorat en Medicine, University of Marseille, Fransa, 1975.
  • Jahn, K.U., Intervall-wertige Mengen, Mathematische Nachrichten, 68, 115-132, 1975.
  • Grattan-Guinness, I., Fuzzy Membership Mapped onto Interval and Many-Valued Quantities, Zeitschrift fur mathematische Logik und Grundladen der Mathematik, 22 (1) 149-160, 1975.
  • Yager, R.R., On the Theory of Bags, Int. J. Gen. Syst., 13 (1), 23-37, 1986.
  • Atanassov, K.T., Intuitionistic Fuzzy Sets, Fuzzy Sets Syst., 20, 87-96, 1986.
  • Smarandache, F., Neutrosophic Set - a Generalization of the Intuitionistic Fuzzy Set, Int. J. Pure Appl. Math., 24 (3), 287-297, 2005.
  • Garibaldi, J.M., Ozen, T., Uncertain Fuzzy Reasoning: a Case Study in Modelling Expert Decision Making. IEEE Trans. Fuzzy Syst., 15 (1), 16-30, 2007.
  • Torra, V., Hesitant Fuzzy Sets, Int. J. Intell. Syst., 25 (6), 529-539, 2010.
  • Yager, R.R., Pythagorean Fuzzy Subsets, 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), Alberta, Canada, 57-61, 24-28 Haziran, 2013.
  • Yager, R.R., Generalized Orthopair Fuzzy Sets, IEEE Trans. Fuzzy Syst., 99, 1-11, 2016.
  • Cuong, B.C., Picture Fuzzy Sets, J. Comput. Sci. Cybern., 30 (4), 409-420, 2014.
  • Senapati, T., Yager, R.R., Fermatean Fuzzy Sets, J. Ambient Intell. Hum. Comput., 11, 663-674, 2020.
  • Atanassov, K.T. Circular Intuitionistic Fuzzy Sets, J. Intell. Fuzzy Syst., 1-6, 2020.
  • Chen, S.-M., Lee, L.-W., Fuzzy Multiple Attributes Group Decision-Making Based on The Ranking Values and the Arithmetic Operations of Interval Type-2 Fuzzy Sets, Expert Syst. Appl., 37 (1), 824-833, 2010.
  • Sanz, J.A., Fernández, A., Bustince, H., Herrera, F., Improving the Performance of Fuzzy Rule-Based Classification Systems with Interval-valued Fuzzy Sets and Genetic Amplitude Tuning, Inf. Sci., 180 (19), 3674-3685, 2010.
  • Ye, F., An Extended TOPSIS Method with Interval-valued Intuitionistic Fuzzy Numbers for Virtual Enterprise Partner Selection, Expert Syst. Appl., 37, 7050-7055, 2010.
  • Kahraman, C., Parchami, A., Cevik Onar, S., Oztaysi, B., Process Capability Analysis Using Intuitionistic Fuzzy Sets, J. Intell. Fuzzy Syst., 32 (3), 1659-1671, 2017.
  • Atanassov, K., Gargov, G., Interval Valued Intuitionistic Fuzzy Sets, Fuzzy Sets Syst., 31, 343-349, 1989.
  • Ahn, J., Han, K., Oh, S., Lee, C., An Application of Interval-Valued Intuitionistic Fuzzy Sets for Medical Diagnosis of Headache, Int. J. Innov. Comput. Inf. Control, 2755-2762, 2011.
  • Mahapatra, G.S., Roy, T.K., Reliability Evaluation Using Triangular Intuitionistic Fuzzy Numbers Arithmetic Operations, World Acad. Sci., Eng. Technol, 26, 574-581, 2009.
  • Kumar, P.S., Hussain, R.J., Computationally Simple Approach for Solving Fully Intuitionistic Fuzzy Real Life Transportation Problems, Int. J. Syst. Assur. Eng. Manage., 7, 90-101, 2016.
  • Kuo, M.-S., Liang, G.-S., A Soft Computing Method of Performance Evaluation with MCDM Based on Interval-valued Fuzzy Numbers, Appl. Soft Comput., 12, 476-485, 2012.
  • Stanujkic, D., Extension of the ARAS Method for Decision-Making Problems with Interval-valued Triangular Fuzzy Numbers, Informatica, 26 (2), 335-355, 2015.
  • Ashtiani, B., Haghighirad, F., Makui, A., Ali Montazer, G., Extension of Fuzzy TOPSIS Method Based on Interval-valued Fuzzy Sets, Appl. Soft Comput., 9, 457-461, 2009.
  • Otay, I., Oztaysi, B., Cevik Onar, S., Kahraman, C., Multi-expert Performance Evaluation of Healthcare Institutions Using an Integrated Intuitionistic Fuzzy AHP&DEA Methodology, 133 (1), 90-106, 2017.
  • Saaty, T.L., The Analytic Hierarchy Process, McGraw-Hill, New York, A.B.D., 1980.
  • Buckley, J.J., Fuzzy Hierarchical Analysis, Fuzzy Sets Syst., 17 (3), 233-247, 1985.
  • Dhillon, B.S., Robot Reliability and Safety, Springer-Verlag, New York, A.B.D., 1991.
  • Olde Keizer, R.A.C.M., van Velsen, L., Moncharmont, M., Riche, B., Ammour, N., Del Signore, S., Zia, G., Hermens, H., N’Dja, A., Using Socially Assistive Robots for Monitoring and Preventing Frailty Among Older Adults: a Study on Usability and User Experience Challenges, Health Technol., 9, 595-605, 2019.
  • Heikkilä, T.,Torvikoski, T., Halme, A., A Solution for the Man/Machine-Interface in Robotics: a High Level Control Language with Enhanced Interaction Equipment, IFAC Proc., 21 (5), 221-225, 1988.
  • Stanujkic, D., Kazimieras Zavadskas, E., Karabasevic, D., Urosevic, S., Maksimovic, M., An Approach for Evaluating Website Quality in Hotel Industry Based on Triangular Intuitionistic Fuzzy Numbers, Informatica, 28 (4), 725-748, 2017.

Sezgisel bulanık çok uzmanlı & çok ölçütlü karar verme metodolojisi: sağlık sektöründe bir uygulama

Yıl 2022, Cilt: 37 Sayı: 2, 1047 - 1062, 28.02.2022
https://doi.org/10.17341/gazimmfd.833468

Öz

Bulanık küme uzantıları son yıllarda çok sayıda araştırmada kullanılan, belirsizliği ele alma araçlarından biridir. Fakat bu uzantılar arasındaki ilişkilerin ve geçişlerin yazında yeteri kadar tartışılmadığı da bir gerçektir. 1990’lı yıllardan itibaren bulanık karar verme teorisi yazında en çok araştırılan konulardan biri olmuştur. Özellikle son yıllarda çok sayıda bulanık karar verme modeli yeni bulanık küme uzantıları kullanılarak önerilmiştir. Çok ölçütlü karar verme modelleri, bu uzantıların sıkça kullanıldığı bir araştırma alanı olmuştur. Bu çalışmada, üçgensel sezgisel bulanık kümeler kullanılarak tanımlanmış bir bütünleşik AHY-TOPSIS metodolojisi, aralık değerli üçgensel bulanık sayıların kullanılabileceği bir metodolojiye dönüştürülmüştür. Uygulama alanı olarak, İstanbul’da faaliyet gösteren bir hastaneler grubunun yeni açacağı hastane için ameliyat robotu değerlendirme ve seçim problemi incelenmiştir. Elde edilen sonuçlar, farklı bulanık küme uzantı tabanlı veri kümelerinin birbirlerine dönüştürülerek aynı metodolojide başarıyla kullanılabileceğini göstermiştir. Bu çalışmada ayrıca karşılaştırma ve duyarlılık analizleri yapılmış ve önerilen modelin sonuçlarının oldukça gürbüz ve tutarlı olduğu gözlemlenmiştir.

Kaynakça

  • Zadeh, L.A., Fuzzy Sets, Inf. Control, 8, 338-353, 1965.
  • Zadeh, L.A., The Concept of a Linguistic Variable and its Application to Approximate Reasoning, Inf. Sci., 8, 199-249, 1975.
  • Sadiq, R., Tesfamariam, S., Environmental Decision-Making Under Uncertainty Using Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP), Stochastic Environ. Res. Risk Assess., 23, 75-91, 2009.
  • Kavita Yadav, S.P. ve Kumar, S., A Multi-criteria Interval-Valued Intuitionistic Fuzzy Group Decision Making for Supplier Selection with TOPSIS Method, RSFDGrC 2009: Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, 5908, Editör: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W., Springer, Berlin, Almanya, 2009.
  • Dammak, F., Baccour, L., Alimi, A.M., Interval Valued Intuitionistic Fuzzy Weight Techniques for TOPSIS Method, 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), Agadir - Morocco, 29 Kasım - 2 Aralık, 2016.
  • Karasan, A., Erdogan, M., Ilbahar, E., Prioritization of Production Strategies of a Manufacturing Plant by Using an Integrated Intuitionistic Fuzzy AHP & TOPSIS Approach, J. Enterp. Inf. Manage., 31 (4), 510-528, 2018.
  • Tooranloo, H.S., Ayatollah, A.S., Iranpour, A., A Model for Supplier Evaluation and Selection Based on Integrated Interval-valued Intuitionistic Fuzzy AHP-TOPSIS Approach, Int. J. Math. Oper. Res., 13 (3), 401-417, 2018.
  • Mousakhani, S., Nazari-Shirkouhi, S., Bozorgi-Amiri, A., A Novel Interval Type-2 Fuzzy Evaluation Model Based Group Decision Analysis for Green Supplier Selection Problems: a Case Study of Battery Industry, J. Cleaner Prod., 168, 205-218, 2017.
  • Toklu, M.C. Interval Type-2 Fuzzy TOPSIS Method for Calibration Supplier Selection Problem: a Case Study in an Automotive Company, Arabian J. Geosci., 11, 341, 2018.
  • Kahraman, C., Öztayşi, B., Cevik Onar, S., Doğan, O., Intuitionistic Fuzzy Originated Interval Type-2 Fuzzy AHP: an Application to Damless Hydroelectric Power Plants, Int. J. Anal. Hierarchy Process, 10(2), 226-292, 2018a.
  • Mei, Y., Xie, K., An Improved TOPSIS Method for Metro Station Evacuation Strategy Selection in Interval Type-2 Fuzzy Environment, Cluster Comput., 22, 2781-2792, 2019.
  • Prabhu, M., Nawzad Abdullah, N., Rzgar Ahmed, R., Nambirajan, T., Pandiyan, S., Segmenting the Manufacturing Industries and Measuring the Performance: Using Interval-valued Triangular Fuzzy TOPSIS Method, Complex Intell. Syst., 6, 591-606, 2020.
  • Sahin, A., Pehlivan, N.Y., Evaluation of Life Quality by Integrated Method of AHP and TOPSIS Based On Interval Type-2 Fuzzy Sets, Hacettepe J. Math. Stat., 46 (3), 511-523, 2017.
  • Wu, Y., Xu, C., Zhang, B., Tao, Y. , Li, X., Chu, H., Liu, F., Sustainability Performance Assessment of Wind Power Coupling Hydrogen Storage Projects Using a Hybrid Evaluation Technique Based on Interval Type-2 Fuzzy Set, Energy, 179, 1176-1190, 2019.
  • Mathew, M., Chakrabortty, R.K. and Ryan, M.J., Selection of an Optimal Maintenance Strategy Under Uncertain Conditions: An Interval Type-2 Fuzzy AHP-TOPSIS Method, IEEE Trans. Eng. Manage., 1-14, 2020.
  • Mardani, A., Hooker, R.E., Ozkul, S., Yifan, S., Nilashi, M., Sabzi, H.Z., Fei, G.C., Application of Decision Making and Fuzzy Sets Theory to Evaluate the Healthcare and Medical Problems: a Review of Three Decades of Research with Recent Developments, Expert Syst. Appl., 137, 202-231, 2019.
  • Kulak, O., Goren, H.G., Supciller, A.A., A New Multi Criteria Decision Making Approach for Medical Imaging Systems Considering Risk Factors, Appl. Soft Comput., 35, 931-941, 2015.
  • Afful-Dadzie, E., Nabareseh, S., Komínková Oplatková, Z., Klímek, P., Model for Assessing Quality of Online Health Information: a Fuzzy VIKOR Based Method, J. Multi-Criteria Decis. Anal., 23, 49-62, 2016.
  • Zhou, F., Wang, X., Goh, M., Fuzzy Extended VIKOR-based Mobile Robot Selection Model for Hospital Pharmacy, Int. J. Adv. Rob. Syst., 1-11, 2018.
  • Abdel-Basset, M., Manogaran, G., Gamal, A., Smarandache, F., A Group Decision Making Framework Based on Neutrosophic TOPSIS Approach for Smart Medical Device Selection, J. Med. Syst., 43, 38, 2019.
  • Büyüközkan, G., Göçer, F., Smart Medical Device Selection Based on Intuitionistic Fuzzy Choquet Integral, Soft Comput., 23, 10085-10103, 2019.
  • Büyüközkan, G., Mukul, E., Evaluation of Smart Health Technologies with Hesitant Fuzzy Linguistic MCDM Methods, J. Intell. Fuzzy Syst., 1-13, 2020.
  • Zadeh, L.A., Is There a Need for Fuzzy Logic?, Inf. Sci., 178 (13), 2751-2779, 2008.
  • Kutlu Gündoğdu, F., Kahraman, C., Spherical Fuzzy Sets and Spherical Fuzzy TOPSIS Method, J. Intell. Fuzzy Syst., 36 (1), 337-352, 2019.
  • Sambuc, R., Function Φ-Flous. Application a l’aide au Diagnostic en Pathologie Thyroidienne, These de Doctorat en Medicine, University of Marseille, Fransa, 1975.
  • Jahn, K.U., Intervall-wertige Mengen, Mathematische Nachrichten, 68, 115-132, 1975.
  • Grattan-Guinness, I., Fuzzy Membership Mapped onto Interval and Many-Valued Quantities, Zeitschrift fur mathematische Logik und Grundladen der Mathematik, 22 (1) 149-160, 1975.
  • Yager, R.R., On the Theory of Bags, Int. J. Gen. Syst., 13 (1), 23-37, 1986.
  • Atanassov, K.T., Intuitionistic Fuzzy Sets, Fuzzy Sets Syst., 20, 87-96, 1986.
  • Smarandache, F., Neutrosophic Set - a Generalization of the Intuitionistic Fuzzy Set, Int. J. Pure Appl. Math., 24 (3), 287-297, 2005.
  • Garibaldi, J.M., Ozen, T., Uncertain Fuzzy Reasoning: a Case Study in Modelling Expert Decision Making. IEEE Trans. Fuzzy Syst., 15 (1), 16-30, 2007.
  • Torra, V., Hesitant Fuzzy Sets, Int. J. Intell. Syst., 25 (6), 529-539, 2010.
  • Yager, R.R., Pythagorean Fuzzy Subsets, 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), Alberta, Canada, 57-61, 24-28 Haziran, 2013.
  • Yager, R.R., Generalized Orthopair Fuzzy Sets, IEEE Trans. Fuzzy Syst., 99, 1-11, 2016.
  • Cuong, B.C., Picture Fuzzy Sets, J. Comput. Sci. Cybern., 30 (4), 409-420, 2014.
  • Senapati, T., Yager, R.R., Fermatean Fuzzy Sets, J. Ambient Intell. Hum. Comput., 11, 663-674, 2020.
  • Atanassov, K.T. Circular Intuitionistic Fuzzy Sets, J. Intell. Fuzzy Syst., 1-6, 2020.
  • Chen, S.-M., Lee, L.-W., Fuzzy Multiple Attributes Group Decision-Making Based on The Ranking Values and the Arithmetic Operations of Interval Type-2 Fuzzy Sets, Expert Syst. Appl., 37 (1), 824-833, 2010.
  • Sanz, J.A., Fernández, A., Bustince, H., Herrera, F., Improving the Performance of Fuzzy Rule-Based Classification Systems with Interval-valued Fuzzy Sets and Genetic Amplitude Tuning, Inf. Sci., 180 (19), 3674-3685, 2010.
  • Ye, F., An Extended TOPSIS Method with Interval-valued Intuitionistic Fuzzy Numbers for Virtual Enterprise Partner Selection, Expert Syst. Appl., 37, 7050-7055, 2010.
  • Kahraman, C., Parchami, A., Cevik Onar, S., Oztaysi, B., Process Capability Analysis Using Intuitionistic Fuzzy Sets, J. Intell. Fuzzy Syst., 32 (3), 1659-1671, 2017.
  • Atanassov, K., Gargov, G., Interval Valued Intuitionistic Fuzzy Sets, Fuzzy Sets Syst., 31, 343-349, 1989.
  • Ahn, J., Han, K., Oh, S., Lee, C., An Application of Interval-Valued Intuitionistic Fuzzy Sets for Medical Diagnosis of Headache, Int. J. Innov. Comput. Inf. Control, 2755-2762, 2011.
  • Mahapatra, G.S., Roy, T.K., Reliability Evaluation Using Triangular Intuitionistic Fuzzy Numbers Arithmetic Operations, World Acad. Sci., Eng. Technol, 26, 574-581, 2009.
  • Kumar, P.S., Hussain, R.J., Computationally Simple Approach for Solving Fully Intuitionistic Fuzzy Real Life Transportation Problems, Int. J. Syst. Assur. Eng. Manage., 7, 90-101, 2016.
  • Kuo, M.-S., Liang, G.-S., A Soft Computing Method of Performance Evaluation with MCDM Based on Interval-valued Fuzzy Numbers, Appl. Soft Comput., 12, 476-485, 2012.
  • Stanujkic, D., Extension of the ARAS Method for Decision-Making Problems with Interval-valued Triangular Fuzzy Numbers, Informatica, 26 (2), 335-355, 2015.
  • Ashtiani, B., Haghighirad, F., Makui, A., Ali Montazer, G., Extension of Fuzzy TOPSIS Method Based on Interval-valued Fuzzy Sets, Appl. Soft Comput., 9, 457-461, 2009.
  • Otay, I., Oztaysi, B., Cevik Onar, S., Kahraman, C., Multi-expert Performance Evaluation of Healthcare Institutions Using an Integrated Intuitionistic Fuzzy AHP&DEA Methodology, 133 (1), 90-106, 2017.
  • Saaty, T.L., The Analytic Hierarchy Process, McGraw-Hill, New York, A.B.D., 1980.
  • Buckley, J.J., Fuzzy Hierarchical Analysis, Fuzzy Sets Syst., 17 (3), 233-247, 1985.
  • Dhillon, B.S., Robot Reliability and Safety, Springer-Verlag, New York, A.B.D., 1991.
  • Olde Keizer, R.A.C.M., van Velsen, L., Moncharmont, M., Riche, B., Ammour, N., Del Signore, S., Zia, G., Hermens, H., N’Dja, A., Using Socially Assistive Robots for Monitoring and Preventing Frailty Among Older Adults: a Study on Usability and User Experience Challenges, Health Technol., 9, 595-605, 2019.
  • Heikkilä, T.,Torvikoski, T., Halme, A., A Solution for the Man/Machine-Interface in Robotics: a High Level Control Language with Enhanced Interaction Equipment, IFAC Proc., 21 (5), 221-225, 1988.
  • Stanujkic, D., Kazimieras Zavadskas, E., Karabasevic, D., Urosevic, S., Maksimovic, M., An Approach for Evaluating Website Quality in Hotel Industry Based on Triangular Intuitionistic Fuzzy Numbers, Informatica, 28 (4), 725-748, 2017.
Toplam 55 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

İrem Otay 0000-0001-5895-506X

Yayımlanma Tarihi 28 Şubat 2022
Gönderilme Tarihi 30 Kasım 2020
Kabul Tarihi 7 Eylül 2021
Yayımlandığı Sayı Yıl 2022 Cilt: 37 Sayı: 2

Kaynak Göster

APA Otay, İ. (2022). Sezgisel bulanık çok uzmanlı & çok ölçütlü karar verme metodolojisi: sağlık sektöründe bir uygulama. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 37(2), 1047-1062. https://doi.org/10.17341/gazimmfd.833468
AMA Otay İ. Sezgisel bulanık çok uzmanlı & çok ölçütlü karar verme metodolojisi: sağlık sektöründe bir uygulama. GUMMFD. Şubat 2022;37(2):1047-1062. doi:10.17341/gazimmfd.833468
Chicago Otay, İrem. “Sezgisel bulanık çok Uzmanlı & çok ölçütlü Karar Verme Metodolojisi: Sağlık sektöründe Bir Uygulama”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 37, sy. 2 (Şubat 2022): 1047-62. https://doi.org/10.17341/gazimmfd.833468.
EndNote Otay İ (01 Şubat 2022) Sezgisel bulanık çok uzmanlı & çok ölçütlü karar verme metodolojisi: sağlık sektöründe bir uygulama. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 37 2 1047–1062.
IEEE İ. Otay, “Sezgisel bulanık çok uzmanlı & çok ölçütlü karar verme metodolojisi: sağlık sektöründe bir uygulama”, GUMMFD, c. 37, sy. 2, ss. 1047–1062, 2022, doi: 10.17341/gazimmfd.833468.
ISNAD Otay, İrem. “Sezgisel bulanık çok Uzmanlı & çok ölçütlü Karar Verme Metodolojisi: Sağlık sektöründe Bir Uygulama”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 37/2 (Şubat 2022), 1047-1062. https://doi.org/10.17341/gazimmfd.833468.
JAMA Otay İ. Sezgisel bulanık çok uzmanlı & çok ölçütlü karar verme metodolojisi: sağlık sektöründe bir uygulama. GUMMFD. 2022;37:1047–1062.
MLA Otay, İrem. “Sezgisel bulanık çok Uzmanlı & çok ölçütlü Karar Verme Metodolojisi: Sağlık sektöründe Bir Uygulama”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 37, sy. 2, 2022, ss. 1047-62, doi:10.17341/gazimmfd.833468.
Vancouver Otay İ. Sezgisel bulanık çok uzmanlı & çok ölçütlü karar verme metodolojisi: sağlık sektöründe bir uygulama. GUMMFD. 2022;37(2):1047-62.