USING CIRCULAR INTUITIONISTIC FUZZY TOPSIS TO ELIMINATE THE HESITANCY IN THE FIELD OF AGRICULTURAL BIOLOGY

Abstract

Circular intuitionistic fuzzy sets (CIFSs) were introduced by Atanassov (2020) as a new extension of intuitionistic fuzzy sets. C-IFSs are represented by a circle of each element that is characterized by degrees of membership and non-membership. In a decision-making process based on experimental data, although uncertainty is low, hesitation can be high. In such cases, the decision-making process is affected by the decision-makers as well as the criteria. Therefore, there is a need to evaluate the expertise of decision-makers within the decision-making process. In Circular Intuitionistic Fuzzy Sets, where hesitation is represented, an approach is proposed for calculating decision-maker weights with the Technique for Order of Preferences by Similarity to Ideal Solution (TOPSIS), which is a multi-criteria decision-making method to eliminate hesitation. In this study, circular intuitionistic fuzzy sets were implemented into the TOPSIS method. The problem was handled from two different perspectives while creating the decision matrix. Sensitivity analyses were performed for both applications. These sensitivity analyses were carried out to examine the change in the ranking of the alternatives when the optimistic or pessimistic approaches of the decision makers, criterion weights, and the decision maker importance weights changed, respectively. In addition, intuitionistic and Pythagorean fuzzy TOPSIS methods were applied and presented as comparative analyses. According to the results obtained, the proposed approaches were satisfactory in eliminating the hesitation.

Keywords

• Circular intuitionistic fuzzy sets
• TOPSIS
• Experimental decision-making problem
• Weights of decision-makers
• Hesitation

References

1. [1] Zadeh L. Fuzzy set. Information and Control 1965; 8(3): 338–353.
2. [2] Zadeh L. The concept of a linguistic variable and its application. Information Sciences 1975; 8(3): 199–249.
3. [3] Atanassov K. Intuitionistic fuzzy sets. Fuzzy Sets System 1986; 20(1): 87–96.
4. [4] Smarandache F. Neutrosophy: Neutrosophic Probability, Set, and Logic: Analytic Synthesis & Synthetic Analysis, American Research Press 1998.
5. [5] Torra V. Hesitant fuzzy sets. International Journal of Intelligent Systems 2010; 25(6): 529–539.
6. [6] Atanassov K. Intuitionistic Fuzzy Sets: Theory and Applications, New York: Heidelberg: Physica-Verlag 1999.
7. [7] Yager R. Pythagorean fuzzy subsets. In: Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013; 57–61.
8. [8] Yager R. Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems 2017; 25(5): 1222−1230.

9. [9] Cuong B. Picture fuzzy sets. Journal of Computer Science and Cybernetics 2014; 30(4): 409–420.
10. [10] Gündoğdu FK, Kahraman C. Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems 2019a; 36(1): 337–352.
11. [11] Atanassov KT. Circular intuitionistic fuzzy sets. Journal of Intelligent & Fuzzy Systems 2020; 39(5): 5981-5986.
12. [12] Kahraman C, Alkan N. Circular intuitionistic fuzzy TOPSIS method with vague membership functions: Supplier selection application context. Notes on Intuitionistic Fuzzy Set 2021; 27(1): 24-52.
13. [13] Büyüközkan G, Göçer F. Smart medical device selection based on intuitionistic fuzzy Choquet integral. Soft Computing 2019; 23(20): 10085−10103.
14. [14] Saaty T. The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, New York: MacGraw-Hill, New-York International Book Company 1980.
15. [15] Saaty T. Decision Making with Dependence and Feedback: The Analytic Network Process, RWS Publications, Pittsburgh 1996.
16. [16] Hwang C, Yoon K. Multiple Attribute Decision Making-Methods, New York: Springer 1981.
17. [17] Opricovic S. Multicriteria Optimization of Civil Engineering Systems. PhD Thesis, Faculty of Civil Engineering, Belgrade 1998.
18. [18] Zolfani SH, Aghdaie MH, Derakhti A, Zavadskas EK, Varzandeh MHM. Decision making on business issues with foresight perspective; an application of new hybrid MCDM model in shopping mall locating. Expert systems with applications 2013; 40(17): 7111-7121.
19. [19] Chen CT. Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems 2000; 114(1): 1-9.
20. [20] Chen T, Tsao C. The interval-valued fuzzy TOPSIS method and experimental analysis. Fuzzy Sets and Systems 2008; 159(11): 1410–1428.
21. [21] Boran F, Genç S, Kurt M, Akay D. A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications 2009; 36(8): 11363–11368.
22. [22] Memari A, Dargi A, Jokar MRA, Ahmad R, Rahim ARA. Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method. Journal of Manufacturing Systems 2019; 50: 9–24.
23. [23] Park J, Park I, Kwun Y, Tan X. Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment. Applied Mathematical Modelling 2011; 35(5): 2544–2556.
24. [24] Tan C. A multi-criteria interval-valued intuitionistic fuzzy group decision making with Choquet integral-based TOPSIS. Expert Systems with Applications 2011; 38(4): 3023–3033.
25. [25] Beg I, Rashid T. TOPSIS for hesitant fuzzy linguistic term sets. International Journal of Intelligent Systems 2013; 28(12): 1162–1171.
26. [26] Xu Z, Zhang X. Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information. Knowledge-Based Systems 2013; 52: 53–64.
27. [27] Biswas P, Pramanik S, Giri B. TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Computing and Applications 2016; 27(3): 727–737.
28. [28] Elhassouny A, Smarandache F. Neutrosophic-simplified-TOPSIS multi-criteria decision-making using combined simplified-TOPSIS method and neutrosophics. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2016; 2468–2474.
29. [29] Zhang X, Xu Z. Extension of TOPSIS to multiple criteria decision making with Pythagorean fuzzy sets. International Journal of Intelligent Systems 2014; 29(12): 1061–1078.
30. [30] Zyoud SH, Fuchs-Hanusch D. A bibliometric-based survey on AHP and TOPSIS techniques. Expert systems with applications 2017; 78: 158-181.
31. [31] Yu C, Shao Y, Wang K, Zhang L. A group decision making sustainable supplier selection approach using extended TOPSIS under interval-valued Pythagorean fuzzy environment. Expert Systems with Applications 2019; 121: 1–17.
32. [32] Sang X, Liu X, Qin J. An analytical solution to fuzzy TOPSIS and its application in personnel selection for knowledge-intensive enterprise. Applied Soft Computing Journal 2015; 30: 190–204.
33. [33] Hussain A, Irfan AM, Mahmood T. Covering based q-rung orthopair fuzzy rough set model hybrid with TOPSIS for multi-attribute decision making. Journal of Intelligent and Fuzzy Systems 2019; 37(1): 981–993.
34. [34] Gündoğdu FK, Kahraman C. A novel fuzzy TOPSIS method using emerging interval-valued spherical fuzzy sets. Engineering Applications of Artificial Intelligence 2019b; 85: 307–323.
35. [35] Senapati Y, Yager R. Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing 2020; 11(2): 663–674.
36. [36] Sajjad Ali Khan M, Abdullah S, Yousaf Ali M, Hussain I, Farooq M. Extension of TOPSIS method base on Choquet integral under interval-valued Pythagorean fuzzy environment. Journal of Intelligent & Fuzzy Systems 2018; 34(1): 267-282.
37. [37] Liang D, Xu Z. The new extension of TOPSIS method for multiple criteria decision making with hesitant Pythagorean fuzzy sets. Applied Soft Computing Journal 2017; 60: 167–179.
38. [38] Budak A, Kaya İ, Karaşan A, Erdoğan M. Real-time location systems selection by using a fuzzy MCDM approach: An application in humanitarian relief logistics. Applied Soft Computing 2020; 92 (106322).
39. [39] Liu H, Rodríguez R. A fuzzy envelope for hesitant fuzzy linguistic term set and its application to multicriteria decision making. Information Sciences 2014; 258: 220–238.
40. [40] Ho L, Lin Y, Chen T. A Pearson-like correlation-based TOPSIS method with interval-valued Pythagorean fuzzy uncertainty and its application to multiple criteria decision analysis of stroke rehabilitation treatments. Neural Computing and Applications 2020; 32(12): 265–295.
41. [41] Alkan N, Kahraman C. Circular intuitionistic fuzzy TOPSIS method: Pandemic hospital location selection. Journal of Intelligent & Fuzzy Systems 2022; 42(1): 295-316.
42. [42] Gökkuş Z, Şentürk S, Alatürk F. Ranking Districts of Çanakkale in Terms of Rangeland Quality by Fuzzy MCDM Methods. Journal of Agricultural, Biological and Environmental Statistics 2023; 28(4): 636-663.
43. [43] Manos B, Moulogianni C, Papathanasiou J, Bournaris T. A fuzzy multicriteria mathematical programming model for planning agricultural regions. New Medit: Mediterranean Journal of Economics, Agriculture and Environment= Revue Méditerranéenne dʹEconomie Agriculture et Environment 2009; 8(4), p22.
44. [44] G wa Mbũgwa G, Prager SD, Krall JM. Utilization of spatial decision support systems decision-making in dryland agriculture: A Tifton burclover case study. Computers and Electronics in Agriculture 2015; 118: 215-224.
45. [45] Mir SA, Padma T. Evaluation and prioritization of rice production practices and constraints under temperate climatic conditions using Fuzzy Analytical Hierarchy Process (FAHP). Spanish journal of agricultural research 2016; 14(4), p22.
46. [46] Jamil M, Sahana M, Sajjad H. Crop suitability analysis in the Bijnor District, UP, using geospatial tools and fuzzy analytical hierarchy process. Agricultural Research 2018; 7(4): 506-522.
47. [47] Hezam IM, Ali AM, Sallam K, Hameed IA, Abdel-Basset M. An efficient decision-making model for evaluating irrigation systems under uncertainty: Toward integrated approaches to sustainability. Agricultural Water Management 2024;303 (109034).
48. [48] Aslan V. Determination of Van Basin Groundwater Potential by GIS Based, AHP and Fuzzy-AHP Methods. Journal of Agricultural Sciences 2024; 30(1): 47-60.
49. [49] Elleuch MA, Anane M, Euchi J, Frikha A. Hybrid fuzzy multi-criteria decision making to solve the irrigation water allocation problem in the Tunisian case. Agricultural systems 2019; 176 (102644).
50. [50] Alaoui ME, Yassini KE, Ben-azza H. Type 2 fuzzy TOPSIS for agriculture MCDM problems. International Journal of Sustainable Agricultural Management and Informatics 2019; 5(2-3): 112-130.
51. [51] Shu MS, Cheng CH, Chang JR. Using intuitionistic fuzzy sets for faulttree analysis on printed circuit board assembly. Microelectronics Reliability 2006; 46(12): 2139–2148.
52. [52] Çakır E, Taş MA. Circular intuitionistic fuzzy decision making and its application. Expert Systems with Applications 2023; 225 (120076).
53. [53] Akram M, Dudek WA, Ilyas F. Group decision‐making based on pythagorean fuzzy TOPSIS method. International Journal of Intelligent Systems 2019; 34(7): 1455-1475.
54. [54] Xu Z. Intuitionistic fuzzy aggregation operators. IEEE Transactions on fuzzy systems 2007; 15(6): 1179-1187.
55. [55] Dowlatshahi MB, Hashemi A. Unsupervised feature selection: A fuzzy multi-criteria decision-making approach. Iranian Journal of Fuzzy Systems 2023; 20(7): 55-70.