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Yeni bir dairesel sezgisel bulanık AHP&VIKOR metodolojisi: Çok uzmanlı tedarikçi değerlendirme problemine uygulama

Yıl 2022, Cilt: 28 Sayı: 1, 194 - 207, 28.02.2022

Öz

Sık kullanılan Çok Ölçütlü Karar Verme (ÇÖKV) yöntemlerinden biri olan VIKOR yöntemi, alternatiflerin pozitif ve negatif ideal çözümlere olan uzaklıklarını temel alır ve uzlaşmacı çözümler sunar. AHP, ölçütlerin ve alternatiflerin ikili olarak karşılaştırılması yoluyla büyük bir problemi küçük ve yönetilebilir problemlere bölen bir başka ÇÖKV yöntemidir. Bu yöntemlerde, ölçüt değerlerinin kesin sayısal atamalarının gerçekten zor olması ve uzmanların düşüncelerini net rakamlarla yansıtamamaları gibi nedenlerle genellikle dilsel değerlendirmeler tercih edilmektedir. Bulanık küme teorisi, bu dilbilimsel değerlendirmelerdeki belirsizlik ve kesin olmama durumlarını bulanık sayıları kullanarak başarıyla ele alır. Dairesel sezgisel bulanık kümeler (D-SBK), Atanassov [1] tarafından tanıtılan sıradan bulanık kümelerin en son uzantısıdır. D-SBK, üyelik (aidiyet) ve üye olmama (aidiyetsizlik) derecelerindeki belirsizlikleri de göz önüne alarak uzmanların bu dereceleri tanımlamalarına yardımcı olur. Bu çalışmada, bütünleşik D-SB AHP ve D-SB VIKOR metodolojisi geliştirilmiş ve çok uzmanlı bir tedarikçi değerlendirme problemine uygulanmıştır. Önerilen metodolojiden elde edilen sonuçlar, diğer yöntemlerle karşılaştırılmakta ve duyarlılık analizi de yapılmaktadır.

Kaynakça

  • [1] Atanassov KT. “Circular intuitionistic fuzzy sets”. Journal of Intelligent & Fuzzy Systems, 39(5), 5981-5986, 2020.
  • [2] Opricovic S, Tzeng GH. “Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS”. European Journal of Operational Research, 156, 445-455, 2004.
  • [3] Zadeh LA. “Fuzzy set”. Information Control, 18(2), 338-353, 1965.
  • [4] Yager RR. “On the theory of bags”. International Journal of General System, 13(1), 23-37, 1986.
  • [5] Atanassov KT. “Intuitionistic fuzzy sets”. Fuzzy Sets and Systems, 20, 87-96, 1986.
  • [6] Cuong BC. “Picture fuzzy sets”. Journal of Computer Science and Cybernetics, 30(4), 409-420, 2014.
  • [7] Kutlu Gündoğdu F, Kahraman C. “Spherical fuzzy sets and spherical fuzzy TOPSIS method”. Journal of Intelligent & Fuzzy Systems, 36(1), 337-352, 2019.
  • [8] Saaty TL. The Analytic Hierarchy Process. New York, USA, McGraw-Hill, 1980.
  • [9] Narayanamoorthy S, Geetha S,Rakkiyappan R, Joo YH. “Interval-valued intuitionistic hesitant fuzzy entropy based VIKOR method for industrial robots selection”. Expert Systems with Applications, 121, 28-37, 2019.
  • [10] Ren Z, Xu Z, Wang H. “Dual hesitant fuzzy VIKOR method for multi-criteria group decision making based on fuzzy measure and new comparison method”. Information Sciences, 388–389, 1-16, 2017.
  • [11] Kahraman C, Öztayşi B, Çevik Onar S. “A comprehensive literature review of 50 years of fuzzy set theory”. International Journal of Computational Intelligence Systems, 9, 3-24, 2016.
  • [12] Van Laarhoven PJM, Pedrycz W. “A fuzzy extension of Saaty’s priority theory”. Fuzzy Sets and Systems, 11, 229-241, 1983.
  • [13] Buckley JJ. “Fuzzy hierarchical analysis”. Fuzzy Sets and Systems, 17(3), 233-247, 1985.
  • [14] Chang DY. “Applications of the extent analysis method on fuzzy AHP”. European Journal of Operational Research, 95, 649-655, 1996.
  • [15] Kahraman C, Oztaysi B, Ucal Sarı I, Turanoğlu E. “Fuzzy analytic hierarchy process with interval type-2 fuzzy sets”. Knowledge-Based Systems, 59, 48-57, 2014.
  • [16] Ayodele TR, Ogunjuyigbe ASO, Odigie O, Munda JL. “A multi-criteria GIS based model for wind farm site selection using interval type-2 fuzzy analytic hierarchy process: The case study of Nigeria”. Applied Energy, 228, 1853-1869, 2018.
  • [17] Oztaysi B, Onar SC, Bolturk E, Kahraman C. “Hesitant fuzzy analytic hierarchy process”. 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Istanbul, Turkey, 02-05 August 2015.
  • [18] Senvar O. “A systematic customer oriented approach based on hesitant fuzzy AHP for performance assessments of service departments”. Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2017 and 16th International Workshop on Intuitionistic Fuzzy sets and Generalized Nets, IWIFSGN 2017, Warsaw, Poland, 11-15 September 2017.
  • [19] Sadiq R, Tesfamariam S. “Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process (IF-AHP)”. Stochastic Environmental Research and Risk Assessment, 23, 75-91, 2009.
  • [20] Xu Z, Liao H. “Intuitionistic fuzzy analytic hierarchy process”. IEEE Transactions on Fuzzy Systems, 22(4), 749-761, 2014.
  • [21] Rouyendegh BD. “Developing an integrated AHP and intuitionistic fuzzy TOPSIS methodology”. Technical Gazette, 21(6), 1313-1319, 2014.
  • [22] Bolturk E, Kahraman C. “A novel interval-valued neutrosophic AHP with cosine similarity measure”. Soft Computing, 22, 4941-4958, 2018.
  • [23] Yazdani M, Torkayesh AE, Stević Ž, Chatterjee P, Ahari SA, Hernandez VD. “An interval valued neutrosophic decisionmaking structure for sustainable supplier selection”. Expert Systems with Applications, 183, 1-19, 2021.
  • [24] Shete PC, Ansari ZN, Kant R. “A Pythagorean fuzzy AHP approach and its application to evaluate the enablers of sustainable supply chain innovation”. Sustainable Production and Consumption, 23, 77-93, 2020.
  • [25] Ayyildiz E, Taskin Gumus A. “Pythagorean fuzzy AHP based risk assessment methodology for hazardous material transportation: an application in Istanbul”. Environmental Science and Pollution Research, 2021. https://doi.org/10.1007/s11356-021-13223-y.
  • [26] Kutlu Gündoğdu F, Kahraman C. Spherical Fuzzy Analytic Hierarchy Process (AHP) and Its Application to Industrial Robot Selection. Editors: Kahraman C, Cebi S, Cevik Onar S, Oztaysi B, Tolga A, Sari I. Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making (INFUS 2019), Advances in Intelligent Systems and Computing, 1029, Springer, Cham, 2019.
  • [27] Dogan O. “Process mining technology selection with spherical fuzzy AHP and sensitivity analysis”. Expert Systems with Applications, 2021. https://doi.org/10.1016/j.eswa.2021.114999.
  • [28] Otay I, Kahraman C. “Solar PV power plant location selection using a Z-fuzzy number based AHP”. International Journal of the Analytic Hierarcy Process, 10(3), 409-430, 2018.
  • [29] Shishavan SA, Donyatalab Y, Farrokhizadeh E. Extension of Classical Analytic Hierarchy Process Using q-Rung Orthopair Fuzzy Sets and Its Application to Disaster Logistics Location Center Selection. Editors: Kahraman C, Cevik Onar S, Oztaysi B, Sari I, Cebi S, Tolga A. Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. (INFUS 2020), Advances in Intelligent Systems and Computing, 1197. Springer, Cham, 2021.
  • [30] Kutlu Gündoğdu F, Duleba S, Moslem S, Aydin S. “Evaluating public transport service quality using picture fuzzy analytic hierarchy process and linear assignment model.” Applied Soft Computing, 2021. https://doi.org/10.1016/j.asoc.2020.106920.
  • [31] Opricovic S. Multicriteria Optimization of Civil Engineering Systems (in Serbian, Visekriterijumska optimizacija sistema u gradjevinarstvu). Ph.D. Thesis, Belgrade: Faculty of Civil Engineering, Belgrade, Serbia, 1998.
  • [32] Opricovic S, Tzeng GH. “The compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS”. European Journal of Operational Research, 156(2), 445-455, 2004.
  • [33] Opricovic S. “A fuzzy compromise solution for multicriteria problems”. International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, 15(3), 363-380, 2007.
  • [34] Ghorabaee MK, Amiri M, Sadaghiani JS, Zavadskas EK. “Multi-criteria project selection using an extended VIKOR method with interval type-2 fuzzy sets”. International Journal of Information Technology & Decision Making, 14(5), 993-1016, 2015.
  • [35] Wang H, Pan X, He S. (2019). “A new interval type-2 fuzzy VIKOR method for multi-attribute decision making”. International Journal of Fuzzy Systems, 21, 145-156, 2019.
  • [36] Liao H, Xu Z. “A VIKOR-based method for hesitant fuzzy multi-criteria decision making”. Fuzzy Optimization and Decision Making, 12, 373-392, 2013.
  • [37] Dong JY, Yuan FF, Wan SP. “Extended VIKOR method for multiple criteria decision-making with linguistic hesitant fuzzy information”. Computers & Industrial Engineering, 112, 305-319, 2017.
  • [38] Devi K. “Extension of VIKOR method in intuitionistic fuzzy environment for robot selection”. Expert Systems with Applications, 38(11), 14163-14168, 2011.
  • [39] Chatterjee K, Kar MB, Kar S. “Strategic decisions using intuitionistic fuzzy VIKOR method for information system (IS) outsourcing”, 2013 International Symposium on Computational and Business Intelligence, New Delhi, India, 24-26 August 2013.
  • [40] Hu J, Pan L, Chen X. “An interval neutrosophic projectionbased VIKOR method for selecting doctors”. Cognitive Computing, 9, 801-816, 2017.
  • [41] Abdel-Basset M, Zhou Y, Mohamed M, Chang V. “A group decision making framework based on neutrosophic VIKOR approach for e-government website evaluation”. Journal of Intelligent & Fuzzy Systems, 34(6), 4213-4224, 2018.
  • [42] Chen TY. “Remoteness index-based Pythagorean fuzzy VIKOR methods with a generalized distance measure for multiple criteria decision analysis”. Information Fusion, 41, 129-150, 2018.
  • [43] Rani P, Mishra AR, Pardasani KR, Mardani A, Liao H, Streimikiene D. “A novel VIKOR approach based on entropy and divergence measures of Pythagorean fuzzy sets to evaluate renewable energy technologies in India”. Journal of Cleaner Production, 2019. https://doi.org/10.1016/j.jclepro.2019.117936.
  • [44] Kutlu Gündoğdu F, Kahraman C, Karaşan A. Spherical Fuzzy VIKOR Method and Its Application to Waste Management. Editors: Kahraman C, Cebi S, Cevik Onar S, Oztaysi B, Tolga A, Sari I. Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making (INFUS 2019), Advances in Intelligent Systems and Computing, 1029, Springer, Cham, 2019.
  • [45] Akram M, Kahraman C, Zahid K. “Group decision-making based on complex spherical fuzzy VIKOR approach”. Knowledge-Based Systems, 2021. https://doi.org/10.1016/j.knosys.2021.106793.
  • [46] Krishankumar R, Gowtham Y, Ahmed I, Ravichandran KS, Kar S. “Solving green supplier selection problem using qrung orthopair fuzzy-based decision framework with unknown weight information”. Applied Soft Computing, 94, 106431, 2020.
  • [47] Cheng S, Jianfu S, Alrasheedi M, Saeidi P, Mishra AR, Rani P. “A new extended VIKOR approach using q-rung orthopair fuzzy sets for sustainable enterprise risk management assessment in manufacturing small and medium-sized enterprises”.International Journal of Fuzzy Systems, 2021, https://doi.org/10.1007/s40815-020-01024-3.
  • [48] Wang L, Zhang HY, Wang JQ, Li L. “Picture fuzzy normalized projection-based VIKOR method for the risk evaluation of construction project”. Applied Soft Computing, 64, 216-226, 2018.
  • [49] Yu C. “Picture fuzzy normalized projection and extended VIKOR approach to software reliability assessment”. Applied Soft Computing Journal, 2020. https://doi.org/10.1016/j.asoc.2019.106056.
  • [50] Atanassov KT. Intuitionistic Fuzzy Sets. Theory and Applications, 1st ed. Heidelberg, Germany, Physica, 1999.
  • [51] Cevik Onar S, Oztaysi B, Otay I, Kahraman C. “Multi-expert wind energy technology selection using interval-valued intuitionistic fuzzy sets”. Energy, 90, 274-285, 2015.
  • [52] Mousavi SM, Vahdani B, Behzadi SS. “Designing a model of intuitionistic fuzzy VIKOR in multi-attribute group decision-making problems”. Iranian Journal of Fuzzy Systems, 13(1),45-65, 2016.
  • [53] Genç S. Intuitionistic Fuzzy Preference Relations and Their Application in Supplier Selection Problem. M.Sc. Thesis, Gazi University, Ankara, Turkey, 2009.
  • [54] Kahraman C, Otay I. “Extension of VIKOR method using circular intuitionistic fuzzy sets”. The International Conference on Intelligent and Fuzzy Systems (INFUS2021), Intelligent and Fuzzy Techniques: Emerging Conditions and Digital Transformation, Izmir, Turkey, 24-26 August 2021.
  • [55] Abdullah L, Najib L. “Sustainable energy planning decision using the intuitionistic fuzzy analytic hierarchy process: choosing energy technology in Malaysia”. International Journal of Sustainable Energy, 35(4), 360-377, 2014.
  • [56] Xu Z. “Intuitionistic fuzzy aggregation operators”. IEEE Transactions on Fuzzy Systems, 15(6), 1179-1187, 2007.
  • [57] Vlachos IK, Sergiadis GD. “Intuitionistic fuzzy information –applications to pattern recognition”. Pattern Recognition Letters, 28(2), 197-206, 2007.
  • [58] Opricovic S. “Fuzzy VIKOR with an application to water resources planning”. Expert Systems with Applications, 38, 12983-12990, 2011.
  • [59] Coskun S, Polat O, Kara B. “A decision model for supplier selection based on business system management and safety criteria and application of the model”. Pamukkale University Journal of Engineering Sciences, 21(4), 134-144, 2015.
  • [60] Sarikaya HA, Caliskan E, Türkbey O. “Fuzzy multiobjective programming model for facility location in an ıntegrated supply chain network”. Pamukkale University Journal of Engineering Sciences, 20(5), 150-161, 2014.
  • [61] Liang X, Chen T, Ye M, Lin H, Li Z. “A hybrid fuzzy BWMVIKOR MCDM to evaluate the service level of bike-sharing companies: A case study from Chengdu, China”. Journal of Cleaner Production, 298, 126759, 2021.
  • [62] Rouyendegh BD. “Developing an Integrated ANP and Intuitionistic Fuzzy TOPSIS Model for Supplier Selection.” Journal of Testing and Evaluation, 43, 664-672, 2015.

A novel circular intuitionistic fuzzy AHP&VIKOR methodology: An application to a multi-expert supplier evaluation problem

Yıl 2022, Cilt: 28 Sayı: 1, 194 - 207, 28.02.2022

Öz

VIKOR method being one of the frequently used Multi-Criteria Decision Making (MCDM) methods, is based on the distances of alternatives to positive and negative ideal solutions, and presents compromising solutions. AHP is another MCDM method dividing the big problem into small and manageable problems through pairwise comparisons of criteria and alternatives. In these methods, linguistic assessments are generally preferred since exact numerical assignments of criteria values are really difficult and experts can not reflect the thoughts in their minds with crisp numbers. The fuzzy set theory captures the vagueness and impreciseness in these linguistic assessments successfully thorough fuzzy numbers. Circular intuitionistic fuzzy sets (C-IFS) are the latest extension of ordinary fuzzy sets, which was introduced by Atanassov [1]. C-IFS help experts to define membership (belongingness) and nonmembership (unbelongingness) degrees by incorporating the uncertainty of these degrees. In this paper, an integrated C-IF AHP & CIF VIKOR methodology is developed and applied to a multi-expert supplier evaluation problem. The results obtained from the proposed methodology are compared with other methods, and a sensitivity analysis is performed as well.

Kaynakça

  • [1] Atanassov KT. “Circular intuitionistic fuzzy sets”. Journal of Intelligent & Fuzzy Systems, 39(5), 5981-5986, 2020.
  • [2] Opricovic S, Tzeng GH. “Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS”. European Journal of Operational Research, 156, 445-455, 2004.
  • [3] Zadeh LA. “Fuzzy set”. Information Control, 18(2), 338-353, 1965.
  • [4] Yager RR. “On the theory of bags”. International Journal of General System, 13(1), 23-37, 1986.
  • [5] Atanassov KT. “Intuitionistic fuzzy sets”. Fuzzy Sets and Systems, 20, 87-96, 1986.
  • [6] Cuong BC. “Picture fuzzy sets”. Journal of Computer Science and Cybernetics, 30(4), 409-420, 2014.
  • [7] Kutlu Gündoğdu F, Kahraman C. “Spherical fuzzy sets and spherical fuzzy TOPSIS method”. Journal of Intelligent & Fuzzy Systems, 36(1), 337-352, 2019.
  • [8] Saaty TL. The Analytic Hierarchy Process. New York, USA, McGraw-Hill, 1980.
  • [9] Narayanamoorthy S, Geetha S,Rakkiyappan R, Joo YH. “Interval-valued intuitionistic hesitant fuzzy entropy based VIKOR method for industrial robots selection”. Expert Systems with Applications, 121, 28-37, 2019.
  • [10] Ren Z, Xu Z, Wang H. “Dual hesitant fuzzy VIKOR method for multi-criteria group decision making based on fuzzy measure and new comparison method”. Information Sciences, 388–389, 1-16, 2017.
  • [11] Kahraman C, Öztayşi B, Çevik Onar S. “A comprehensive literature review of 50 years of fuzzy set theory”. International Journal of Computational Intelligence Systems, 9, 3-24, 2016.
  • [12] Van Laarhoven PJM, Pedrycz W. “A fuzzy extension of Saaty’s priority theory”. Fuzzy Sets and Systems, 11, 229-241, 1983.
  • [13] Buckley JJ. “Fuzzy hierarchical analysis”. Fuzzy Sets and Systems, 17(3), 233-247, 1985.
  • [14] Chang DY. “Applications of the extent analysis method on fuzzy AHP”. European Journal of Operational Research, 95, 649-655, 1996.
  • [15] Kahraman C, Oztaysi B, Ucal Sarı I, Turanoğlu E. “Fuzzy analytic hierarchy process with interval type-2 fuzzy sets”. Knowledge-Based Systems, 59, 48-57, 2014.
  • [16] Ayodele TR, Ogunjuyigbe ASO, Odigie O, Munda JL. “A multi-criteria GIS based model for wind farm site selection using interval type-2 fuzzy analytic hierarchy process: The case study of Nigeria”. Applied Energy, 228, 1853-1869, 2018.
  • [17] Oztaysi B, Onar SC, Bolturk E, Kahraman C. “Hesitant fuzzy analytic hierarchy process”. 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Istanbul, Turkey, 02-05 August 2015.
  • [18] Senvar O. “A systematic customer oriented approach based on hesitant fuzzy AHP for performance assessments of service departments”. Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2017 and 16th International Workshop on Intuitionistic Fuzzy sets and Generalized Nets, IWIFSGN 2017, Warsaw, Poland, 11-15 September 2017.
  • [19] Sadiq R, Tesfamariam S. “Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process (IF-AHP)”. Stochastic Environmental Research and Risk Assessment, 23, 75-91, 2009.
  • [20] Xu Z, Liao H. “Intuitionistic fuzzy analytic hierarchy process”. IEEE Transactions on Fuzzy Systems, 22(4), 749-761, 2014.
  • [21] Rouyendegh BD. “Developing an integrated AHP and intuitionistic fuzzy TOPSIS methodology”. Technical Gazette, 21(6), 1313-1319, 2014.
  • [22] Bolturk E, Kahraman C. “A novel interval-valued neutrosophic AHP with cosine similarity measure”. Soft Computing, 22, 4941-4958, 2018.
  • [23] Yazdani M, Torkayesh AE, Stević Ž, Chatterjee P, Ahari SA, Hernandez VD. “An interval valued neutrosophic decisionmaking structure for sustainable supplier selection”. Expert Systems with Applications, 183, 1-19, 2021.
  • [24] Shete PC, Ansari ZN, Kant R. “A Pythagorean fuzzy AHP approach and its application to evaluate the enablers of sustainable supply chain innovation”. Sustainable Production and Consumption, 23, 77-93, 2020.
  • [25] Ayyildiz E, Taskin Gumus A. “Pythagorean fuzzy AHP based risk assessment methodology for hazardous material transportation: an application in Istanbul”. Environmental Science and Pollution Research, 2021. https://doi.org/10.1007/s11356-021-13223-y.
  • [26] Kutlu Gündoğdu F, Kahraman C. Spherical Fuzzy Analytic Hierarchy Process (AHP) and Its Application to Industrial Robot Selection. Editors: Kahraman C, Cebi S, Cevik Onar S, Oztaysi B, Tolga A, Sari I. Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making (INFUS 2019), Advances in Intelligent Systems and Computing, 1029, Springer, Cham, 2019.
  • [27] Dogan O. “Process mining technology selection with spherical fuzzy AHP and sensitivity analysis”. Expert Systems with Applications, 2021. https://doi.org/10.1016/j.eswa.2021.114999.
  • [28] Otay I, Kahraman C. “Solar PV power plant location selection using a Z-fuzzy number based AHP”. International Journal of the Analytic Hierarcy Process, 10(3), 409-430, 2018.
  • [29] Shishavan SA, Donyatalab Y, Farrokhizadeh E. Extension of Classical Analytic Hierarchy Process Using q-Rung Orthopair Fuzzy Sets and Its Application to Disaster Logistics Location Center Selection. Editors: Kahraman C, Cevik Onar S, Oztaysi B, Sari I, Cebi S, Tolga A. Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. (INFUS 2020), Advances in Intelligent Systems and Computing, 1197. Springer, Cham, 2021.
  • [30] Kutlu Gündoğdu F, Duleba S, Moslem S, Aydin S. “Evaluating public transport service quality using picture fuzzy analytic hierarchy process and linear assignment model.” Applied Soft Computing, 2021. https://doi.org/10.1016/j.asoc.2020.106920.
  • [31] Opricovic S. Multicriteria Optimization of Civil Engineering Systems (in Serbian, Visekriterijumska optimizacija sistema u gradjevinarstvu). Ph.D. Thesis, Belgrade: Faculty of Civil Engineering, Belgrade, Serbia, 1998.
  • [32] Opricovic S, Tzeng GH. “The compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS”. European Journal of Operational Research, 156(2), 445-455, 2004.
  • [33] Opricovic S. “A fuzzy compromise solution for multicriteria problems”. International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, 15(3), 363-380, 2007.
  • [34] Ghorabaee MK, Amiri M, Sadaghiani JS, Zavadskas EK. “Multi-criteria project selection using an extended VIKOR method with interval type-2 fuzzy sets”. International Journal of Information Technology & Decision Making, 14(5), 993-1016, 2015.
  • [35] Wang H, Pan X, He S. (2019). “A new interval type-2 fuzzy VIKOR method for multi-attribute decision making”. International Journal of Fuzzy Systems, 21, 145-156, 2019.
  • [36] Liao H, Xu Z. “A VIKOR-based method for hesitant fuzzy multi-criteria decision making”. Fuzzy Optimization and Decision Making, 12, 373-392, 2013.
  • [37] Dong JY, Yuan FF, Wan SP. “Extended VIKOR method for multiple criteria decision-making with linguistic hesitant fuzzy information”. Computers & Industrial Engineering, 112, 305-319, 2017.
  • [38] Devi K. “Extension of VIKOR method in intuitionistic fuzzy environment for robot selection”. Expert Systems with Applications, 38(11), 14163-14168, 2011.
  • [39] Chatterjee K, Kar MB, Kar S. “Strategic decisions using intuitionistic fuzzy VIKOR method for information system (IS) outsourcing”, 2013 International Symposium on Computational and Business Intelligence, New Delhi, India, 24-26 August 2013.
  • [40] Hu J, Pan L, Chen X. “An interval neutrosophic projectionbased VIKOR method for selecting doctors”. Cognitive Computing, 9, 801-816, 2017.
  • [41] Abdel-Basset M, Zhou Y, Mohamed M, Chang V. “A group decision making framework based on neutrosophic VIKOR approach for e-government website evaluation”. Journal of Intelligent & Fuzzy Systems, 34(6), 4213-4224, 2018.
  • [42] Chen TY. “Remoteness index-based Pythagorean fuzzy VIKOR methods with a generalized distance measure for multiple criteria decision analysis”. Information Fusion, 41, 129-150, 2018.
  • [43] Rani P, Mishra AR, Pardasani KR, Mardani A, Liao H, Streimikiene D. “A novel VIKOR approach based on entropy and divergence measures of Pythagorean fuzzy sets to evaluate renewable energy technologies in India”. Journal of Cleaner Production, 2019. https://doi.org/10.1016/j.jclepro.2019.117936.
  • [44] Kutlu Gündoğdu F, Kahraman C, Karaşan A. Spherical Fuzzy VIKOR Method and Its Application to Waste Management. Editors: Kahraman C, Cebi S, Cevik Onar S, Oztaysi B, Tolga A, Sari I. Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making (INFUS 2019), Advances in Intelligent Systems and Computing, 1029, Springer, Cham, 2019.
  • [45] Akram M, Kahraman C, Zahid K. “Group decision-making based on complex spherical fuzzy VIKOR approach”. Knowledge-Based Systems, 2021. https://doi.org/10.1016/j.knosys.2021.106793.
  • [46] Krishankumar R, Gowtham Y, Ahmed I, Ravichandran KS, Kar S. “Solving green supplier selection problem using qrung orthopair fuzzy-based decision framework with unknown weight information”. Applied Soft Computing, 94, 106431, 2020.
  • [47] Cheng S, Jianfu S, Alrasheedi M, Saeidi P, Mishra AR, Rani P. “A new extended VIKOR approach using q-rung orthopair fuzzy sets for sustainable enterprise risk management assessment in manufacturing small and medium-sized enterprises”.International Journal of Fuzzy Systems, 2021, https://doi.org/10.1007/s40815-020-01024-3.
  • [48] Wang L, Zhang HY, Wang JQ, Li L. “Picture fuzzy normalized projection-based VIKOR method for the risk evaluation of construction project”. Applied Soft Computing, 64, 216-226, 2018.
  • [49] Yu C. “Picture fuzzy normalized projection and extended VIKOR approach to software reliability assessment”. Applied Soft Computing Journal, 2020. https://doi.org/10.1016/j.asoc.2019.106056.
  • [50] Atanassov KT. Intuitionistic Fuzzy Sets. Theory and Applications, 1st ed. Heidelberg, Germany, Physica, 1999.
  • [51] Cevik Onar S, Oztaysi B, Otay I, Kahraman C. “Multi-expert wind energy technology selection using interval-valued intuitionistic fuzzy sets”. Energy, 90, 274-285, 2015.
  • [52] Mousavi SM, Vahdani B, Behzadi SS. “Designing a model of intuitionistic fuzzy VIKOR in multi-attribute group decision-making problems”. Iranian Journal of Fuzzy Systems, 13(1),45-65, 2016.
  • [53] Genç S. Intuitionistic Fuzzy Preference Relations and Their Application in Supplier Selection Problem. M.Sc. Thesis, Gazi University, Ankara, Turkey, 2009.
  • [54] Kahraman C, Otay I. “Extension of VIKOR method using circular intuitionistic fuzzy sets”. The International Conference on Intelligent and Fuzzy Systems (INFUS2021), Intelligent and Fuzzy Techniques: Emerging Conditions and Digital Transformation, Izmir, Turkey, 24-26 August 2021.
  • [55] Abdullah L, Najib L. “Sustainable energy planning decision using the intuitionistic fuzzy analytic hierarchy process: choosing energy technology in Malaysia”. International Journal of Sustainable Energy, 35(4), 360-377, 2014.
  • [56] Xu Z. “Intuitionistic fuzzy aggregation operators”. IEEE Transactions on Fuzzy Systems, 15(6), 1179-1187, 2007.
  • [57] Vlachos IK, Sergiadis GD. “Intuitionistic fuzzy information –applications to pattern recognition”. Pattern Recognition Letters, 28(2), 197-206, 2007.
  • [58] Opricovic S. “Fuzzy VIKOR with an application to water resources planning”. Expert Systems with Applications, 38, 12983-12990, 2011.
  • [59] Coskun S, Polat O, Kara B. “A decision model for supplier selection based on business system management and safety criteria and application of the model”. Pamukkale University Journal of Engineering Sciences, 21(4), 134-144, 2015.
  • [60] Sarikaya HA, Caliskan E, Türkbey O. “Fuzzy multiobjective programming model for facility location in an ıntegrated supply chain network”. Pamukkale University Journal of Engineering Sciences, 20(5), 150-161, 2014.
  • [61] Liang X, Chen T, Ye M, Lin H, Li Z. “A hybrid fuzzy BWMVIKOR MCDM to evaluate the service level of bike-sharing companies: A case study from Chengdu, China”. Journal of Cleaner Production, 298, 126759, 2021.
  • [62] Rouyendegh BD. “Developing an Integrated ANP and Intuitionistic Fuzzy TOPSIS Model for Supplier Selection.” Journal of Testing and Evaluation, 43, 664-672, 2015.
Toplam 62 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makine Müh. / Endüstri Müh.
Yazarlar

İrem Otay Bu kişi benim

Cengiz Kahraman Bu kişi benim

Yayımlanma Tarihi 28 Şubat 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 28 Sayı: 1

Kaynak Göster

APA Otay, İ., & Kahraman, C. (2022). A novel circular intuitionistic fuzzy AHP&VIKOR methodology: An application to a multi-expert supplier evaluation problem. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 194-207.
AMA Otay İ, Kahraman C. A novel circular intuitionistic fuzzy AHP&VIKOR methodology: An application to a multi-expert supplier evaluation problem. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. Şubat 2022;28(1):194-207.
Chicago Otay, İrem, ve Cengiz Kahraman. “A Novel Circular Intuitionistic Fuzzy AHP&VIKOR Methodology: An Application to a Multi-Expert Supplier Evaluation Problem”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 28, sy. 1 (Şubat 2022): 194-207.
EndNote Otay İ, Kahraman C (01 Şubat 2022) A novel circular intuitionistic fuzzy AHP&VIKOR methodology: An application to a multi-expert supplier evaluation problem. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 28 1 194–207.
IEEE İ. Otay ve C. Kahraman, “A novel circular intuitionistic fuzzy AHP&VIKOR methodology: An application to a multi-expert supplier evaluation problem”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 28, sy. 1, ss. 194–207, 2022.
ISNAD Otay, İrem - Kahraman, Cengiz. “A Novel Circular Intuitionistic Fuzzy AHP&VIKOR Methodology: An Application to a Multi-Expert Supplier Evaluation Problem”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 28/1 (Şubat 2022), 194-207.
JAMA Otay İ, Kahraman C. A novel circular intuitionistic fuzzy AHP&VIKOR methodology: An application to a multi-expert supplier evaluation problem. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2022;28:194–207.
MLA Otay, İrem ve Cengiz Kahraman. “A Novel Circular Intuitionistic Fuzzy AHP&VIKOR Methodology: An Application to a Multi-Expert Supplier Evaluation Problem”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 28, sy. 1, 2022, ss. 194-07.
Vancouver Otay İ, Kahraman C. A novel circular intuitionistic fuzzy AHP&VIKOR methodology: An application to a multi-expert supplier evaluation problem. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2022;28(1):194-207.





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