RESEARCH ARTICLE
A group decision-making algorithm considering interaction and feedback mechanisms for dynamic supplier selection under q-rung orthopair fuzzy information
Zaoli Yang,
Tingting Zhang,
Salman Ahmad,
Shivam Gupta,
Corresponding Author
Zaoli Yang
College of Economics and Management, Beijing University of Technology, Beijing, China
Correspondence Zaoli Yang, College of Economics and Management, Beijing University of Technology, No. 100 Pingleyuan, Chaoyang District 100124, Beijing, China.
Email: [email protected]
Search for more papers by this authorTingting Zhang
College of Economics and Management, Beijing University of Technology, Beijing, China
Search for more papers by this authorSalman Ahmad
School of Business and Creative Industries, University of the West of Scotland, Ayr, Scotland, UK
Search for more papers by this authorShivam Gupta
Department of Information Systems, Supply Chain Management & Decision Support, NEOMA Business School, Reims, France
Search for more papers by this authorZaoli Yang,
Tingting Zhang,
Salman Ahmad,
Shivam Gupta,
Corresponding Author
Zaoli Yang
College of Economics and Management, Beijing University of Technology, Beijing, China
Correspondence Zaoli Yang, College of Economics and Management, Beijing University of Technology, No. 100 Pingleyuan, Chaoyang District 100124, Beijing, China.
Email: [email protected]
Search for more papers by this authorTingting Zhang
College of Economics and Management, Beijing University of Technology, Beijing, China
Search for more papers by this authorSalman Ahmad
School of Business and Creative Industries, University of the West of Scotland, Ayr, Scotland, UK
Search for more papers by this authorShivam Gupta
Department of Information Systems, Supply Chain Management & Decision Support, NEOMA Business School, Reims, France
Search for more papers by this authorAbstract
Supplier selection is vital for enterprises to operate stably and achieve and sustain a competitive advantage. However, from the initial establishment to the gradual development and maturity of the enterprise, the supplier selection criteria change dynamically, and decision-makers are hardly in agreement in each stage, which creates challenges for enterprises when choosing suitable suppliers. As such, this paper proposes a multicriteria group decision-making method based on an interaction and feedback mechanism (IFM) and q-rung orthopair fuzzy sets theory. We introduce an IFM to achieve consensus among different decision-makers. We then develop the q-rung orthopair fuzzy weighted partitioned Bonferroni mean (q-ROFWPBM) operator to address the aggregation problem of dynamic multicriteria. A group decision-making algorithm combining the IFM and q-ROFWPBM operator is proposed to analyze the Hongxing Erke supplier selection. The results show that the proposed method can not only account for large differences of opinion among decision-makers during group decision-making but also consider the dynamic changes of supplier selection criteria in different stages of enterprise development and help enterprises choose suppliers suitable for their own development characteristics.
CONFLICT OF INTERESTS
The authors declare that there are no conflict of interests.
Open Research
DATA AVAILABILITY STATEMENT
The authors confirm that the data supporting the findings of this study are available within the article.
REFERENCES
- 1Blome C, Hollos D, Paulraj A. Green procurement and green supplier development: antecedents and effects on supplier performance. Int J Prod Econ. 2014; 124: 252-264. Int J Prod Econ.
- 2Guo ZX, Liu HT, Zhang DQ, Yang J. Green supplier evaluation and selection in apparel manufacturing using a fuzzy multi-criteria decision-making approach. Sustainability. 2017; 9(4): 650.
- 3Chen YG, Wang S, Yao JR, Li YX, Yang SQ. Socially responsible supplier selection and sustainable supply chain development: a combined approach of total interpretive structural modeling and fuzzy analytic network process. Bus Strategy Environ. 2018; 27(8): 1708-1719.
- 4Wang CN, Huang YF, Cheng I, Nguyen VT. A multi-criteria decision-making (MCDM) approach using hybrid SCOR metrics, AHP, and TOPSIS for supplier evaluation and selection in the gas and oil industry. Processes. 2018; 6(12): 252.
- 5Wang CN, Yang CY, Cheng HC. A fuzzy multicriteria decision-making (MCDM) model for sustainable supplier evaluation and selection based on triple bottom line approaches in the garment industry. Processes. 2019; 7(7): 400.
10.3390/pr7070400 Google Scholar
- 6Kusi-Sarpong S, Leonilde Varela M, Putnik G, Avila P, Agyemang J. Supplier evaluation and selection: a fuzzy novel multicriteria group decision-making approach. Int J Qual Res. 2018; 12(2): 459-486.
- 7Chen ZH, Ming XG, Zhou TT, Chang Y. Sustainable supplier selection for smart supply chain considering internal and external uncertainty: an integrated rough-fuzzy approach. Appl Soft Comput. 2020; 87: 106004.
10.1016/j.asoc.2019.106004 Google Scholar
- 8Liu PD, Wang P. Some interval-valued intuitionistic fuzzy Schweizer–Sklar power aggregation operators and their application to supplier selection. Int J Syst Sci. 2018; 49(6): 1188-1211.
10.1080/00207721.2018.1442510 Google Scholar
- 9Pang QH, Yang TT, Li MZ, Shen Y. A fuzzy-grey multicriteria decision making approach for green supplier selection in low-carbon supply chain. Math Probl Eng. 2017; 2017: 9653261-9653269.
10.1155/2017/9653261 Google Scholar
- 10Rabbani M, Foroozesh N, Mousavi SM, Farrokhi-Asl H. Sustainable supplier selection by a new decision model based on interval-valued fuzzy sets and possibilistic statistical reference point systems under uncertainty. Int J Syst Sci—Oper. 2019; 6(2): 162-178.
- 11Tooranloo HS, Iranpour A. Supplier selection and evaluation using interval-valued intuitionistic fuzzy AHP method. Int J Procure Manage. 2017; 10(5): 539-554.
10.1504/IJPM.2017.086399 Google Scholar
- 12Wang R, Li YL. A novel approach for green supplier selection under a q-rung orthopair fuzzy environment. Symmetry. 2018; 10(12): 687.
- 13Liu YM, Jin LL, Zhu F. A multi-criteria group decision making model for green supplier selection under the ordered weighted hesitant fuzzy environment. Symmetry. 2019; 11(1): 17.
10.3390/sym11010017 Google Scholar
- 14Guarnieri P, Trojan F. Decision making on supplier selection based on social, ethical, and environmental criteria: a study in the textile industry. Resour Conserv Recycl. 2019; 141: 347-361.
- 15Kaya R, Yet B. Building Bayesian networks based on DEMATEL for multiple criteria decision problems: a supplier selection case study. Expert Syst Appl. 2019; 134: 234-248.
- 16Lo HW, Liou JJH, Wang HS, Tsai YS. An integrated model for solving problems in green supplier selection and order allocation. J Clean Prod. 2018; 190: 339-352.
- 17Amiri M, Hashemi-Tabatabaei M, Ghahremanloo M, Keshavarz-Ghorabaee M, Zavadskas EK, Banaitis A. A new fuzzy BWM approach for evaluating and selecting a sustainable supplier in supply chain management. Int J Sust Dev World. 2021; 28(2): 125-142.
10.1080/13504509.2020.1793424 Google Scholar
- 18Song W, Xu Z, Liu HC. Developing sustainable supplier selection criteria for solar air-conditioner manufacturer: an integrated approach. Renew Sust Energy Rev. 2017; 79: 1461-1471.
- 19Liou JJH, Chuang YC, Zavadskas EK, Tzeng GH. Data-driven hybrid multiple attribute decision-making model for green supplier evaluation and performance improvement. J Clean Prod. 2019; 241: 118321.
10.1016/j.jclepro.2019.118321 Google Scholar
- 20Chen KS, Wang CH, Tan KH. Developing a fuzzy green supplier selection model using six sigma quality indices. Int J Prod Econ. 2019; 212: 1-7.
- 21Giannakis M, Dubey R, Vlachos I, Ju YB. Supplier sustainability performance evaluation using the analytic network process. J Clean Prod. 2020; 247: 119439.
10.1016/j.jclepro.2019.119439 Google Scholar
- 22Zhang H, Cui Y. A model combining a Bayesian network with a modified genetic algorithm for green supplier selection. Simulation. 2019; 95(12): 1165-1183.
10.1177/0037549719826306 Google Scholar
- 23He XS, Zhang J. Supplier selection study under the respective of low-carbon supply chain: a hybrid evaluation model based on FA-DEA-AHP. Sustainability. 2018; 10(2): 564.
10.3390/su10020564 Google Scholar
- 24Phochanikorn P, Tan CQ. An integrated multi-criteria decision-making model based on prospect theory for green supplier selection under uncertain environment: a case study of the Thailand palm oil products industry. Sustainability. 2019; 11(7):1872.
10.3390/su11071872 Google Scholar
- 25Zadeh LA. Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers. In: GJ Klir, B Yuan, eds. Vol. 6. World Scientific; 1996: 394–432.
- 26Atanassov K. Intuitionistic fuzzy sets. Int J Bioautom. 2016; 20: 1.
- 27Jiang W, Wei BY, Liu X, Li XY, Zheng HQ. Intuitionistic fuzzy power aggregation operator based on entropy and its application in decision making. Int J Intell Syst. 2018; 33(1): 49-67.
- 28Luo MX, Zhao RR. A distance measure between intuitionistic fuzzy sets and its application in medical diagnosis. Artif Intell Med. 2018; 89: 34-39.
- 29Ngan RT, Ali M, Tamir DE, Rishe ND, Kandel A. Representing complex intuitionistic fuzzy set by quaternion numbers and applications to decision making. Appl Soft Comput. 2020; 87:105961.
- 30Kumar PS. Algorithms for solving the optimization problems using fuzzy and intuitionistic fuzzy set. Int J Syst Assur Eng. 2020; 11(1): 189-222.
10.1007/s13198-019-00941-3 Google Scholar
- 31Thirumalai C, Senthilkumar M. An assessment framework of intuitionistic fuzzy network for C2B decision making. In: 2017 4th International Conference on Electronics and Communication Systems (ICECS). IEEE; 2017:164-167.
- 32Garg H, Kumar K. Improved possibility degree method for ranking intuitionistic fuzzy numbers and their application in multiattribute decision-making. Granular Comput. 2019; 4(2): 237-247.
10.1007/s41066-018-0092-7 Google Scholar
- 33Yager RR. Pythagorean membership grades in multicriteria decision making. IEEE Trans Fuzzy Syst. 2013; 22(4): 958-965.
- 34Yager RR. Generalized orthopair fuzzy sets. IEEE Trans Fuzzy Syst. 2016; 25(5): 1222-1230.
- 35Zhang C, Liao HC, Luo L. Additive consistency-based priority-generating method of q-rung orthopair fuzzy preference relation. Int J Intell Syst. 2019; 34: 2151-2176.
- 36Peng XD, Krishankumar R, Ravichandran KS. Generalized orthopair fuzzy weighted distance-based approximation (WDBA) algorithm in emergency decision-making. Int J Intell Syst. 2019; 34: 2364-2402.
- 37Liu DH, Peng D, Liu ZM. The distance measures between q-rung orthopair hesitant fuzzy sets and their application in multiple criteria decision making. Int J Intell Syst. 2019; 34: 2104-2121.
- 38Hussain A, Ali MI, Mahmood T. Covering based q-rung orthopair fuzzy rough set model hybrid with TOPSIS for multi-attribute decision making. J Intell Fuzzy Syst. 2019; 37: 981-993.
- 39Wang J, Wei GW, Wei C, Wei Y. Dual hesitant q-rung orthopair fuzzy Muirhead mean operators in multiple attribute decision making. IEEE Access. 2019; 7: 67139-67166.
- 40Chen K, Luo YD. Generalized orthopair linguistic Muirhead mean operators and their application in multi-criteria decision making. J Intell Fuzzy Syst. 2019; 37: 797-809.
10.3233/JIFS-181366 Google Scholar
- 41Wang J, Wei G, Lu J, et al. Some q-rung orthopair fuzzy Hamy mean operators in multiple attribute decision-making and their application to enterprise resource planning systems selection. Int J Intell Syst. 2019; 34: 2429-2458.
- 42Liu ZM, Wang S, Liu PD. Multiple attribute group decision making based on q-rung orthopair fuzzy Heronian mean operators. Int J Intell Syst. 2018; 33: 2341-2363.
- 43Liu PD, Wang P. Multiple-attribute decision-making based on Archimedean Bonferroni operators of q-rung orthopair fuzzy numbers. IEEE Trans Fuzzy Syst. 2019; 27: 834-848.
- 44Yang W, Pang YF. New q-rung orthopair fuzzy partitioned Bonferroni mean operators and their application in multiple attribute decision making. Int J Intell Syst. 2019; 34: 439-476.
- 45Bonferroni C. Sulle medie multiple di potenze. Boll Mat Ital. 1950; 5: 267-270.
- 46Wang J, Wei GW, Wei Y. Models for green supplier selection with some 2-tuple linguistic neutrosophic number Bonferroni mean operators. Symmetry. 2018; 10(5): 131.
- 47Liu PD, Teng F. Some interval neutrosophic hesitant uncertain linguistic Bonferroni mean aggregation operators for multiple attribute decision-making. Int J Uncertainty Quan. 2017; 7(6): 525-572.
10.1615/Int.J.UncertaintyQuantification.2017020094 Google Scholar
- 48Liu PD, Zhang LL, Liu X, Wang P. Multi-valued neutrosophic number Bonferroni mean operators with their applications in multiple attribute group decision making. Int J Inf Tech Decis. 2016; 15(05): 1181-1210.
10.1142/S0219622016500346 Google Scholar
- 49Wei GW, Wang R, Wang J, Wei C, Zhang Y. Methods for evaluating the technological innovation capability for the high-tech enterprises with generalized interval neutrosophic number Bonferroni mean operators. IEEE Access. 2019; 7: 86473-86492.
10.1109/ACCESS.2019.2925702 Google Scholar
- 50Liu PD, Chen SM, Liu JL. Multiple attribute group decision making based on intuitionistic fuzzy interaction partitioned Bonferroni mean operators. Inf Sci. 2017; 411: 98-121.
- 51Liu PD, Liu JL, Merigó JM. Partitioned Heronian means based on linguistic intuitionistic fuzzy numbers for dealing with multi-attribute group decision making. Appl Soft Comput. 2018; 62: 395-422.
- 52Liu P, Chen SM, Wang Y. Multiattribute group decision making based on intuitionistic fuzzy partitioned Maclaurin symmetric mean operators. Inf Sci. 2020; 512: 830-854.
- 53Garg H, Chen SM. Multiattribute group decision making based on neutrality aggregation operators of q-rung orthopair fuzzy sets. Inf Sci. 2020; 517: 427-447.
- 54Mu ZM, Zeng SZ, Wang PY. Novel approach to multi-attribute group decision-making based on interval-valued Pythagorean fuzzy power Maclaurin symmetric mean operator. Comput Ind Eng. 2021; 155: 107049.
- 55Liu PD, Ali Z, Mahmood T. A method to multi-attribute group decision-making problem with complex q-rung orthopair linguistic information based on Heronian mean operators. Int J Comput Int Sys. 2019; 12(2): 1465-1496.
- 56Liu PD, Liu JL. Some q-rung orthopair fuzzy Bonferroni mean operators and their application to multi-attribute group decision making. Int J Intell Syst. 2018; 33(2): 315-347.
- 57Li ZX, Gao H, Wei GW. Methods for multiple attribute group decision making based on intuitionistic fuzzy Dombi Hamy mean operators. Symmetry. 2018; 10(11): 574.
- 58Zhang ZM. Trapezoidal interval type-2 fuzzy aggregation operators and their application to multiple attribute group decision making. Neural Comput Appl. 2018; 29(4): 1039-1054.
10.1007/s00521-016-2488-0 Google Scholar
- 59Hendiani S, Jiang LS, Sharifi E, Liao HC. Multi-expert multi-criteria decision making based on the likelihoods of interval type-2 trapezoidal fuzzy preference relations. Int J Mach Learn Cybern. 2020; 11(12): 2719-2741.
10.1007/s13042-020-01148-w Google Scholar
- 60Chen SM, Hong JA. Fuzzy multiple attributes group decision-making based on ranking interval type-2 fuzzy sets and the TOPSIS method. IEEE Trans Syst Man Cybern: Syst. 2014; 44(12): 1665-1673.
- 61Xu XH, Du ZJ, Chen XH, Cai CG. Confidence consensus-based model for large-scale group decision making: a novel approach to managing non-cooperative behaviors. Inf Sci. 2019; 477: 410-427.
- 62Urena R, Kou G, Dong YC, Chiclana F, Herrera-Viedma E. A review on trust propagation and opinion dynamics in social networks and group decision making frameworks. Inf Sci. 2019; 478: 461-475.
- 63Xiao J, Wang XL, Zhang HJ. Managing personalized individual semantics and consensus in linguistic distribution large-scale group decision making. Inf Fusion. 2020; 53: 20-34.
- 64Xu YJ, Wen XW, Zhang WC. A two-stage consensus method for large-scale multi-attribute group decision making with an application to earthquake shelter selection. Comput Ind Eng. 2018; 116: 113-129.
- 65Chao XR, Kou G, Peng Y, Viedma EH. Large-scale group decision-making with non-cooperative behaviors and heterogeneous preferences: an application in financial inclusion. Eur J Oper Res. 2021; 288(1): 271-293.
10.1016/j.ejor.2020.05.047 Google Scholar
- 66Gou XJ, Xu ZS, Herrera F. Consensus reaching process for large-scale group decision making with double hierarchy hesitant fuzzy linguistic preference relations. Knowl-Based Syst. 2018; 157: 20-33.
- 67Tang M, Zhou XY, Liao HC, Xu JP, Fujita H, Herrera F. Ordinal consensus measure with objective threshold for heterogeneous large-scale group decision making. Knowl-Based Syst. 2019; 180: 62-74.
- 68Wu ZB, Xu JP. A consensus model for large-scale group decision making with hesitant fuzzy information and changeable clusters. Inf Fusion. 2018; 41: 217-231.
- 69Yager RR. Generalized orthopair fuzzy sets. IEEE Trans Fuzzy Syst. 2017; 25(5): 1222-1230.
- 70Liu PD, Wang P. Some q-rung orthopair fuzzy aggregation operators and their applications tomultiple-attribute decision making. Int J Intell Syst. 2018; 33(2): 259-280.
- 71Pérez LG, Mata F, Chiclana F. Social network decision making with linguistic trustworthiness-based induced OWA operators. Int J Intell Syst. 2015; 29: 1117-1137.
10.1002/int.21686 Google Scholar
- 72Liao HC, Xu ZS, Zeng XJ, Merigo JM. Framework of group decision making with intuitionistic fuzzy preference information. IEEE Trans Fuzzy Syst. 2014; 23(4): 1211-1227.
10.1109/TFUZZ.2014.2348013 Google Scholar
- 73Mao XB, Wu M. Probabilistic linguistic multi-attribute group decision making model based on interaction and feedback. Fuzzy Syst Math. 2019; 33(03): 134-143.
- 74Dutta B, Guha D. Partitioned Bonferroni mean based on linguistic 2-tuple for dealing with multi-attribute group decision making. Appl Soft Comput. 2015; 37: 166-179.
- 75Gao HX, Ju YB, Gonzalez EDRS, Zhang WK. Green supplier selection in electronics manufacturing: an approach based on consensus decision making. J Clean Prod. 2020; 245:118781.
- 76Lu ZM, Sun XK, Wang YX, Xu CB. Green supplier selection in straw biomass industry based on cloud model and possibility degree. J Clean Prod. 2019; 209: 995-1005.
