An approach for supplier selection problem based on picture cubic fuzzy aggregation operators

This article is an advanced approach to picture fuzzy set through the application of cubic set theory. For instance, we establish the idea of the picture cubic fuzzy sets (PCFSs) theory and define several operations for PCFS. Also, presented some weighted aggregation operators under picture cubic fuzzy information, so called picture cubic fuzzy weighted averaging (PCFWA) operator, picture cubic fuzzy order weighted averaging (PCFOWA) operator, picture cubic fuzzy weighted geometric (PCFWG) operator, and picture cubic fuzzy order weighted geometric (PCFOWG) operator. Further, we study their fundamental properties and showed the relationship among these aggregation operators. In order to determine the feasibility and practicality of the mentioned new technique, we developed multi-attribute group decision -making algorithm with picture cubic fuzzy environment. Further, the developed method applied to supply chain management and for implementation, consider numerical application of supply chain management. Compared the developed approach with other preexisting aggregation operators, and we concluded that the defined technique is better, reliable and effective.

[1]  Harish Garg,et al.  Some Generalized Complex Intuitionistic Fuzzy Aggregation Operators and Their Application to Multicriteria Decision-Making Process , 2018, Arabian Journal for Science and Engineering.

[2]  Saleem Abdullah,et al.  Picture fuzzy aggregation information based on Einstein operations and their application in decision making , 2019, Mathematical Sciences.

[3]  Jing Xiao,et al.  Linguistic Distribution-Based Optimization Approach for Large-Scale GDM With Comparative Linguistic Information: An Application on the Selection of Wastewater Disinfection Technology , 2020, IEEE Transactions on Fuzzy Systems.

[4]  Syeda Tayyba Tehrim,et al.  Cubic bipolar fuzzy ordered weighted geometric aggregation operators and their application using internal and external cubic bipolar fuzzy data , 2019, Comput. Appl. Math..

[5]  I. Coyne,et al.  Reframing the focus from a family-centred to a child-centred care approach for children’s healthcare , 2016, Journal of child health care : for professionals working with children in the hospital and community.

[6]  Harish Garg,et al.  Decision-making model under complex picture fuzzy Hamacher aggregation operators , 2020, Computational and Applied Mathematics.

[7]  Le Hoang Son,et al.  Picture inference system: a new fuzzy inference system on picture fuzzy set , 2016, Applied Intelligence.

[8]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[9]  Zeshui Xu,et al.  Managing hesitant Information in GDM Problems under fuzzy and Multiplicative Preference Relations , 2013, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[10]  Peng Wang,et al.  Some Improved Linguistic Intuitionistic Fuzzy Aggregation Operators and Their Applications to Multiple-Attribute Decision Making , 2017, Int. J. Inf. Technol. Decis. Mak..

[11]  Le Hoang Son DPFCM: A novel distributed picture fuzzy clustering method on picture fuzzy sets , 2015, Expert Syst. Appl..

[12]  Zhen Zhang,et al.  Consensus reaching for MAGDM with multi-granular hesitant fuzzy linguistic term sets: a minimum adjustment-based approach , 2019, Annals of Operations Research.

[13]  Jun Ye,et al.  Multiple attribute decision-making method based on linguistic cubic variables , 2018, J. Intell. Fuzzy Syst..

[14]  Saleem Abdullah,et al.  Trapezoidal cubic fuzzy number Einstein hybrid weighted averaging operators and its application to decision making , 2019, Soft Comput..

[15]  Marion Oswald,et al.  Algorithm-assisted decision-making in the public sector: framing the issues using administrative law rules governing discretionary power , 2018, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[16]  Lazim Abdullah,et al.  Logarithmic Aggregation Operators of Picture Fuzzy Numbers for Multi-Attribute Decision Making Problems , 2019, Mathematics.

[17]  Harish Garg,et al.  Group Decision Algorithm for Aged Healthcare Product Purchase Under q-Rung Picture Normal Fuzzy Environment Using Heronian Mean Operator , 2020, Int. J. Comput. Intell. Syst..

[18]  Young Bae Jun,et al.  Cubic Interval-Valued Intuitionistic Fuzzy Sets and Their Application in BCK/BCI-Algebras , 2018, Axioms.

[19]  Le Hoang Son Generalized picture distance measure and applications to picture fuzzy clustering , 2016, Appl. Soft Comput..

[20]  Changyong Liang,et al.  Multi-criteria group decision making method based on generalized intuitionistic trapezoidal fuzzy prioritized aggregation operators , 2015, International Journal of Machine Learning and Cybernetics.

[21]  Peide Liu,et al.  Some Similarity Measures for Interval-Valued Picture Fuzzy Sets and Their Applications in Decision Making , 2019, Inf..

[22]  Le Hoang Son Measuring analogousness in picture fuzzy sets: from picture distance measures to picture association measures , 2017, Fuzzy Optim. Decis. Mak..

[23]  Pham Van Hai,et al.  Some Fuzzy Logic Operators for Picture Fuzzy Sets , 2015, 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE).

[24]  Saleem Abdullah,et al.  Novel Concept of Cubic Picture Fuzzy Sets , 2018 .

[25]  Madhumangal Pal,et al.  Picture fuzzy Dombi aggregation operators: Application to MADM process , 2019, Appl. Soft Comput..

[26]  Guiwu Wei,et al.  Picture fuzzy aggregation operators and their application to multiple attribute decision making , 2017, J. Intell. Fuzzy Syst..

[27]  Guiwu Wei,et al.  Picture fuzzy cross-entropy for multiple attribute decision making problems , 2016 .

[28]  Zhen Zhang,et al.  Consensus reaching for social network group decision making by considering leadership and bounded confidence , 2020, Knowl. Based Syst..

[29]  K. Govindan,et al.  Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach , 2018 .

[30]  Wen Cao,et al.  q‐Rung orthopair fuzzy Choquet integral aggregation and its application in heterogeneous multicriteria two‐sided matching decision making , 2019, Int. J. Intell. Syst..

[31]  Yong Deng,et al.  Evidential Supplier Selection Based on DEMATEL and Game Theory , 2018, Int. J. Fuzzy Syst..

[32]  S. Meysam Mousavi,et al.  Soft computing-based new interval-valued hesitant fuzzy multi-criteria group assessment method with last aggregation to industrial decision problems , 2017, Soft Comput..

[33]  Vladik Kreinovich,et al.  A classification of representable t-norm operators for picture fuzzy sets , 2016, 2016 Eighth International Conference on Knowledge and Systems Engineering (KSE).

[34]  Yong Yang,et al.  Adjustable soft discernibility matrix based on picture fuzzy soft sets and its applications in decision making , 2015, J. Intell. Fuzzy Syst..

[35]  Guiwu Wei,et al.  Picture Fuzzy Hamacher Aggregation Operators and their Application to Multiple Attribute Decision Making , 2018, Fundam. Informaticae.

[36]  Bui Cong Cuong,et al.  Picture fuzzy sets , 2015 .

[37]  G. Wei,et al.  SOME SIMILARITY MEASURES FOR PICTURE FUZZY SETS AND THEIR APPLICATIONS , 2018 .

[38]  Le Hoang Son,et al.  Picture fuzzy clustering for complex data , 2016, Eng. Appl. Artif. Intell..

[39]  Abdullah Al Khaled,et al.  A hybrid ensemble and AHP approach for resilient supplier selection , 2019, J. Intell. Manuf..

[40]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[41]  Gagandeep Kaur,et al.  TOPSIS based on nonlinear-programming methodology for solving decision-making problems under cubic intuitionistic fuzzy set environment , 2019, Computational and Applied Mathematics.

[42]  Narges Banaeian,et al.  Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry , 2018, Comput. Oper. Res..

[43]  Gagandeep Kaur,et al.  Extended TOPSIS method for multi-criteria group decision-making problems under cubic intuitionistic fuzzy environment , 2018, Scientia Iranica.

[44]  Hui Gao,et al.  The Generalized Dice Similarity Measures for Picture Fuzzy Sets and Their Applications , 2018, Informatica.

[45]  Harish Garg,et al.  New Operations on Interval-Valued Picture Fuzzy Set, Interval-Valued Picture Fuzzy Soft Set and Their Applications , 2019, IEEE Access.

[46]  Le Hoang Son,et al.  A New Approach to Multi-variable Fuzzy Forecasting Using Picture Fuzzy Clustering and Picture Fuzzy Rule Interpolation Method , 2014, KSE.

[47]  Harish Garg Some Picture Fuzzy Aggregation Operators and Their Applications to Multicriteria Decision-Making , 2017, Arabian Journal for Science and Engineering.

[48]  Le Hoang Son,et al.  A novel automatic picture fuzzy clustering method based on particle swarm optimization and picture composite cardinality , 2016, Knowl. Based Syst..

[49]  Huayou Chen,et al.  Generalized Ordered Weighted Proportional Averaging Operator and Its Application to Group Decision Making , 2014, Informatica.

[50]  Le Hoang Son,et al.  THEORETICAL ANALYSIS OF PICTURE FUZZY CLUSTERING: CONVERGENCE AND PROPERTY , 2018, Journal of Computer Science and Cybernetics.

[51]  Gagandeep Kaur,et al.  Multi-Attribute Decision-Making Based on Bonferroni Mean Operators under Cubic Intuitionistic Fuzzy Set Environment , 2018, Entropy.

[52]  Xinyue Kou,et al.  Consistency improvement for fuzzy preference relations with self-confidence: An application in two-sided matching decision making , 2020, J. Oper. Res. Soc..

[53]  Fuad E. Alsaadi,et al.  Projection models for multiple attribute decision making with picture fuzzy information , 2016, International Journal of Machine Learning and Cybernetics.

[54]  Harish Garg,et al.  Linguistic Interval-Valued Pythagorean Fuzzy Sets and Their Application to Multiple Attribute Group Decision-making Process , 2020, Cognitive Computation.

[55]  Gagandeep Kaur,et al.  Generalized Cubic Intuitionistic Fuzzy Aggregation Operators Using t-Norm Operations and Their Applications to Group Decision-Making Process , 2018, Arabian Journal for Science and Engineering.

[56]  Pushpinder Singh,et al.  Correlation coefficients for picture fuzzy sets , 2015, J. Intell. Fuzzy Syst..