Abstract
This study proposes a trapezoidal interval type-2 fuzzy TOPSIS method to evaluate agricultural risk management tools. The weighted centroids based on AHP is used in the proposed method to defuzzify the fuzzy weighted ratings of each alternative versus each criterion. By this way, this method considers all centroid values of the upper and lower membership functions and uses the weight as a parameter to consider the relative importance of controid values of the upper and lower membership functions. Additionally, the left and the right upper membership function and lower membership function of the multiplication of two positive trapezoidal IT2FNs can be developed based on the α-cuts. The proposed method is applied to help farmers compare agricultural risk management tools and select the most suitable one. This is the first study using fuzzy decision-making methods to help farmers evaluate agricultural risk management tools. To show the proposed method’s feasibility, a numerical example is displayed. Finally, some comparisons are made to show the advantages of the proposed method, and an experiment is conducted to show the robustness of the proposed method.
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References
Akram M, Adeel A (2019) TOPSIS approach for MAGDM based on interval-valued hesitant fuzzy N-soft environment. Int J Fuzzy Syst 21(3):993–1009
Akram M, Shumaiza, Arshad M (2020) Bipolar fuzzy TOPSIS and bipolar fuzzy ELECTRE-I methods to diagnosis. Comput Appl Math 39:7. https://doi.org/10.1007/s40314-019-0980-8
Akram M, Dudek WA, Ilyas F (2019) Group decision-making based on pythagorean fuzzy TOPSIS method. Int J Intell Syst 34(7):1455–1475
Akram M, Garg H, Zahid K (2020) Extensions of ELECTRE-I and TOPSIS methods for group decision-making under complex pythagorean fuzzy environment. Iran J Fuzzy Syst 17(5):147–164
Akram M, Kahraman C, Zahid K (2021a) Extension of TOPSIS model to the decision-making under complex spherical fuzzy information. Soft Comput 25(16):10771–10795
Akram M, Luqman A, Alcantud JCR (2021b) Risk evaluation in failure modes and effects analysis: hybrid TOPSIS and ELECTRE I solutions with pythagorean fuzzy information. Neural Comput Appl 33(11):5675–5703
Aktan HE, Samut PK (2013) Agricultural performance evaluation by integrating fuzzy AHP and VIKOR methods. Int J Appl Decis Sci 6(4):324–344
Antón J (2008) Agricultural policies and risk management: a holistic approach. In: 108th EAAE seminar ‘income stabilisation in a changing agricultural world: policy and tools’, Warsaw, Poland
Banihabib ME, Shabestari MH (2016) Fuzzy hybrid MCDM model for ranking the agricultural water demand management strategies in arid areas. Water Resour Manage 31(1):495–513
Baykasoğlu A, Gölcük İ (2017) Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS. Expert Syst Appl 70:37–51
Bera AK, Jana DK, Banerjee D, Nandy T (2020) Supplier selection using extended IT2 fuzzy TOPSIS and IT2 fuzzy MOORA considering subjective and objective factors. Soft Comput 24(12):8899–8915
Bielza M, Garrido A, Sumpsi JM (2007) Finding optimal price risk management instruments: the case of the Spanish potato sector. Agric Econ 36(1):67–78
Breen B, Hennessy T, Donnellan T, Hanrahan K (2013) Tools and polices for agricultural risk management. In: Agricultural economics society AES 87th annual conference, April 8–10, 2013. Warwick University, Coventry, vol 84, pp 487–492
Büyüközkan G, Parlak IB, Tolga AC (2016) Evaluation of knowledge management tools by using an interval type-2 fuzzy TOPSIS method. Int J Comput Intell Syst 9(5):812–826
Celik E, Akyuz E (2018) An interval type-2 fuzzy AHP and TOPSIS methods for decision-making problems in maritime transportation engineering: the case of ship loader. Ocean Eng 155(7):371–381
Celik E, Gul M, Aydin N, Gumus AT, Guneri AF (2015) A comprehensive review of multi criteria decision making approaches based on interval type-2 fuzzy sets. Knowl-Based Syst 85:329–341
Chatterjee K, Kar S (2016) Multi-criteria analysis of supply chain risk management using interval valued fuzzy TOPSIS. Ops—Sopus-Scimago 53(3):474–499
Chen TY (2013) A signed-distance-based approach to importance assessment and multi-criteria group decision analysis based on interval type-2 fuzzy set. Knowl Inf Syst 35(1):193–231
Chen SJ, Chen SM (2007) Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. Appl Intell 26(1):1–11
Chen SM, Lee LW (2010a) 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
Chen SM, Lee LW (2010b) Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method. Expert Syst Appl 37(4):2790–2798
Chen CT, Lin CT, Huang SF (2006) A fuzzy approach for supplier evaluation and selection in supply chain management. Int J Prod Econ 102(2):289–301
Cheng CH (1998) A new approach for ranking fuzzy numbers by distance method. Fuzzy Sets Syst 95(3):307–317
Cheng RC, Kang B, Zhang J (2022) A novel method to rank fuzzy numbers using the developed golden rule representative value. Appl Intell. https://doi.org/10.1007/s10489-021-02965-4
Chi HTX, Yu VF (2018) Ranking generalized fuzzy numbers based on centroid and rank index. Appl Soft Comput J 68:283–292
Chu TC, Le THP (2022) Evaluating and selecting agricultural insurance packages through an AHP-based fuzzy TOPSIS method. Soft Comput. https://doi.org/10.1007/s00500-022-06964-6
Chu TC, Yeh WC (2018) Fuzzy multiple criteria decision-making via an inverse function-based total utility approach. Soft Comput 22(22):7423–7433
De A, Kundu P, Das S, Kar S (2020) A ranking method based on interval type-2 fuzzy sets for multiple attribute group decision making. Soft Comput 24:131–154
Demeke M, Kiermeier M, Sow M (2016) Antonaci L (2016) Agriculture and food insecurity risk management in Africa. Food and Agriculture Organization of the United Nations, Rome
Deveci M, Demirel NÇ, Ahmetoğlu E (2017) Airline new route selection based on interval type-2 fuzzy MCDM: a case study of new route between Turkey-North American region destinations. J Air Transp Manag 59(3):83–99
Deveci M, Canıtez F, Gökaşar I (2018) WASPAS and TOPSIS based interval type-2 fuzzy MCDM method for a selection of a car sharing station. Sustain Cities Soc 41(2):777–791
Dymova L, Sevastjanov P, Tikhonenko A (2015) An interval type-2 fuzzy extension of the TOPSIS method using alpha cuts. Knowl-Based Syst 83(1):116–127
Gómez-Limón JA, Arriaza M, Riesgo L (2003) An MCDM analysis of agricultural risk aversion. Eur J Oper Res 151(3):569–585
Hart CE, Babcock BA (2001) Rankings of risk management strategies combining crop insurance products and marketing positions. CARD Working Papers, 294
Hwang CL, Yoon K (1981) Multiple attribute decision making: methods and applications : a state-of-the-art survey. Springer, New York
Kaufmann A, Gupta MM (1985) Introduction to fuzzy arithmetic: theory and application. Van Nostrand Reinhold, New York
Kisaka-Lwayo M, Obi A (2012) Risk perceptions and management strategies by smallholder farmers in KwaZulu-Natal Province, South Africa. Int J Agric Manag 01(3):28–39
Liang C, Wu J, Zhang J (2006) Ranking indices and rules for fuzzy numbers based on gravity center point. Proc World Congr Intell Control Autom (WCICA) 1:3159–3163
Liao TW (2015) Two interval type 2 fuzzy TOPSIS material selection methods. Mater Des 88:1088–1099
Liu P, Jin F (2012) A multi-attribute group decision-making method based on weighted geometric aggregation operators of interval-valued trapezoidal fuzzy numbers. Appl Math Model 36(6):2498–2509
Mei Y, Xie K (2017) An improved TOPSIS method for metro station evacuation strategy selection in interval type-2 fuzzy environment. Clust Comput 22:2781–2792
Mendel JM, John RIB (2002) Type-2 fuzzy sets made simple. IEEE Trans Fuzzy Syst 10(2):117–127
Mendel JM, John RI, Liu F (2006) Interval type-2 fuzzy logic systems made simple. IEEE Trans Fuzzy Syst 14(6):808–821
Meuwissen MPM, Huirne RBM, Hardaker JB (2001) Risk and risk management: an empirical analysis of Dutch livestock farmers. Livest Prod Sci 69(1):43–53
Morales C, Garrido A, Pálinkás P (2008) Risks perceptions and risk management instruments in the European Union: Do farmers have a clear idea of what they need ? In: 12th Congress of the European Association of Agricultural Economists, pp 1–8
Mousakhani S, Nazari-Shirkouhi S, Bozorgi-Amiri A (2017) A novel interval type-2 fuzzy evaluation model based group decision analysis for green supplier selection problems: a case study of battery industry. J Clean Prod 168:205–218
Murakami S, Maeda H, Imamura S (1984) Fuzzy decision analysis in the development of centralized regional energy control systems. Energy Dev Jpn 6(4):379–396
Orojloo M, Shahdany SMH, Roozbahani A (2018) Developing an integrated risk management framework for agricultural water conveyance and distribution systems within fuzzy decision making approaches. Sci Total Environ 627:1363–1376
Park JH, Park IY, Kwun YC, Tan X (2011) Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment. Appl Math Model 35(5):2544–2556
Rohde UL, Jain GC, Poddar AK, Ghosh AK (2012) Introduction to integral calculus. Wiley
Saaty TL (1980) The analytic hierarchy process. McGraw-Hill Book Co., New York
Saqib S, Ahmad MM, Panezai S, Ali U (2016) Factors influencing farmers’ adoption of agricultural credit as a risk management strategy: the case of Pakistan. Int J Disaster Risk Reduct 17:67–76
Sharaf IM (2019) An interval type-2 fuzzy TOPSIS using the extended vertex method for MAGDM. SN Appl Sci 2:87. https://doi.org/10.1007/s42452-019-1825-1
Toledo R, Engler A, Ahumada V (2011) Evaluation of risk factors in agriculture: an application of the analytical hierarchical process (AHP) methodology. Chil J Agric Res 71(1):114–121
Topaloglu M, Yarkin F, Kaya T (2018) Solid waste collection system selection for smart cities based on a type-2 fuzzy multi-criteria decision technique. Soft Comput 22(15):4879–4890
Tudor K, Spaulding A, Roy KD, Winter R (2014) An analysis of risk management tools utilized by Illinois farmers. Agric Financ Rev 74(1):69–86
Velandia M, Rejesus RM, Knight TO, Sherrick BJ (2009) Factors affecting farmers’ utilization of agricultural risk management tools: the case of crop insurance, forward contracting, and spreading sales. J Agric Appl Econ 41(1):107–123
Wang YJ, Lee HS (2008) The revised method of ranking fuzzy numbers with an area between the centroid and original points. Comput Math Appl 55(9):2033–2042
Wang YM, Yang JB, Xu DL, Chin KS (2006) On the centroids of fuzzy numbers. Fuzzy Sets Syst 157(7):919–926
Yager RR (1978) Ranking fuzzy subsets over the unit interval. In: 1978 IEEE conference on decision and control including the 17th symposium on adaptive processes, pp 1435–1437
Yang YY, Liu XW, Liu F (2020) Trapezoidal interval type-2 fuzzy TOPSIS using alpha-cuts. Int J Fuzzy Syst 22(1):293–309
Yu VF, Van LH, Dat LQ, Chi HTX, Chou SY, Duong TTT (2017) Analyzing the ranking method for fuzzy numbers in fuzzy decision making based on the magnitude concepts. Int J Fuzzy Syst 19:1279–1289
Yucesan M, Mete S, Serin F, Celik E, Gul M (2019) An integrated best-worst and interval type-2 fuzzy TOPSIS methodology for green supplier selection. Mathematics 7:182
Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-I. Inf Sci 8(3):199–249
Acknowledgements
The authors would like to thank the two anonymous referees and the Academic Editor for providing helpful comments. Their insights and suggestions led to a better presentation of the ideas expressed in this paper. This paper was partially supported by the National Science and Technology Council, Taiwan, under Grant MOST 111-2410-H-218-004.
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The funding was partially provided by the National Science and Technology Council, Taiwan (Grant No. MOST 111-2410-H-218-004).
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Appendices
Appendix A
Theorem
Let \(q\) and \(w\) be two trapezoidal IT2FNs, \(q,w \in R^{ + } , \, and\)\({\text{ q}} = ((a_{1}^{{\text{U}}} ,a_{2}^{{\text{U}}} ,a_{3}^{{\text{U}}} ,a_{4}^{{\text{U}}} ;H_{q}^{{\text{U}}} ), \, (a_{1}^{{\text{L}}} ,a_{2}^{{\text{L}}} ,a_{3}^{{\text{L}}} ,a_{4}^{{\text{L}}} ;H_{q}^{{\text{L}}} ))\), \(w = ((b_{1}^{{\text{U}}} ,b_{2}^{{\text{U}}} ,b_{3}^{{\text{U}}} ,b_{4}^{{\text{U}}} ;H_{w}^{{\text{U}}} ),(b_{1}^{{\text{L}}} ,b_{2}^{{\text{L}}} ,b_{3}^{{\text{L}}} ,b_{4}^{{\text{L}}} ;H_{w}^{{\text{L}}} ))\). The multiplication of \(q\) and \(w\) is defined by the upper membership function \(\overline{f}_{q \otimes w}^{{}} (x)\), including left upper membership function \(\overline{f}_{q \otimes w}^{L} (x)\) and the right upper membership function \(\overline{f}_{q \otimes w}^{R} (x)\), and lower membership functions \(\underline {f}_{q \otimes w} (x)\), including left lower membership function \(\underline {f}_{q \otimes w}^{L} (x)\) and the right lower membership function \(\underline {f}_{q \otimes w}^{R} (x)\), as follows.
and
where \(E_{1} = (a_{2}^{{\text{U}}} - a_{1}^{{\text{U}}} )(b_{2}^{{\text{U}}} - b_{1}^{{\text{U}}} )\), \(F_{1} = a_{1}^{{\text{U}}} ((b_{2}^{{\text{U}}} - b_{1}^{{\text{U}}} ) + b_{1}^{{\text{U}}} (a_{2}^{{\text{U}}} - a_{1}^{{\text{U}}} )\), \(E_{2} = (a_{3}^{{\text{U}}} - a_{4}^{{\text{U}}} {)}(b_{3}^{{\text{U}}} - b_{4}^{{\text{U}}} )\),\(F_{2} = a_{4}^{{\text{U}}} (b_{3}^{{\text{U}}} - b_{4}^{{\text{U}}} ) + b_{4}^{{\text{U}}} (a_{3}^{{\text{U}}} - a_{4}^{{\text{U}}} {)}\), \(G_{1} = a_{1}^{{\text{U}}} b_{1}^{{\text{U}}}\), \(G_{2} = a_{2}^{{\text{U}}} b_{2}^{{\text{U}}}\), \(G_{3} = a_{3}^{{\text{U}}} b_{3}^{{\text{U}}}\), \(G_{4} = a_{4}^{{\text{U}}} b_{4}^{{\text{U}}}\), \(H_{q \otimes w}^{{\text{U}}} = \min (H_{q}^{{\text{U}}} ,H_{w}^{{\text{U}}} )\); \(E_{3} = (a_{2}^{{\text{L}}} - a_{1}^{{\text{L}}} )(b_{2}^{{\text{L}}} - b_{1}^{{\text{L}}} )\), \(F_{3} = a_{1}^{{\text{L}}} (b_{2}^{{\text{L}}} - b_{1}^{{\text{L}}} ) + b_{1}^{{\text{L}}} (a_{2}^{{\text{L}}} - a_{1}^{{\text{L}}} )\), \(E_{4} = (a_{3}^{{\text{L}}} - a_{4}^{{\text{L}}} )(b_{3}^{{\text{L}}} - b_{4}^{{\text{L}}} )\),\(F_{4} = a_{4}^{{\text{L}}} (b_{3}^{{\text{L}}} - b_{4}^{{\text{L}}} ) + b_{4}^{{\text{L}}} (a_{3}^{{\text{L}}} - a_{4}^{{\text{L}}} )\), \(Q_{1} = a_{1}^{{\text{L}}} b_{1}^{{\text{L}}}\), \(Q_{2} = a_{2}^{{\text{L}}} b_{2}^{{\text{L}}}\), \(Q_{3} = a_{3}^{{\text{L}}} b_{3}^{{\text{L}}}\), \(Q_{4} = a_{4}^{{\text{L}}} b_{4}^{{\text{L}}}\), \(H_{q \otimes w}^{{\text{L}}} = \min (H_{q}^{{\text{L}}} ,H_{w}^{{\text{L}}} )\).
Proof
By using Eq. (3) and Eqs. (14)–(22) based on \(\alpha {\text{ - cuts}}\) and interval arithemetic operations of fuzzy numbers, the upper membership function \(\overline{f}_{q \otimes w}^{{}} (x)\), including \(\overline{f}_{{v_{ij} }}^{L} (x)\) and \(\overline{f}_{{v_{ij} }}^{R} (x)\), and lower membership functions \(\underline {f}_{q \otimes w} (x)\), including \(\underline {f}_{{v_{ij} }}^{L} (x)\) and \(\underline {f}_{{v_{ij} }}^{R} (x)\), can be developed as shown in the theorem; in which \(\overline{f}_{q \otimes w}^{L} (x) = \frac{{ - F_{1} - \sqrt {F_{1}^{2} - 4E_{1} (G_{1} - x)} }}{{2E_{1} }}\) and \(\underline {f}_{q \otimes w}^{L} (x) = \frac{{ - F_{3} - \sqrt {F_{3}^{2} - 4E_{3} (Q_{1} - x)} }}{{2E_{3} }}\) are not allowed because negative grades are not allowed.where \(E_{1} = (a_{2}^{{\text{U}}} - a_{1}^{{\text{U}}} )(b_{2}^{{\text{U}}} - b_{1}^{{\text{U}}} )\), \(F_{1} = a_{1}^{{\text{U}}} ((b_{2}^{{\text{U}}} - b_{1}^{{\text{U}}} ) + b_{1}^{{\text{U}}} (a_{2}^{{\text{U}}} - a_{1}^{{\text{U}}} )\), \(E_{2} = (a_{3}^{{\text{U}}} - a_{4}^{{\text{U}}} {)}(b_{3}^{{\text{U}}} - b_{4}^{{\text{U}}} )\),\(F_{2} = a_{4}^{{\text{U}}} (b_{3}^{{\text{U}}} - b_{4}^{{\text{U}}} ) + b_{4}^{{\text{U}}} (a_{3}^{{\text{U}}} - a_{4}^{{\text{U}}} {)}\), \(G_{1} = a_{1}^{{\text{U}}} b_{1}^{{\text{U}}}\), \(G_{2} = a_{2}^{{\text{U}}} b_{2}^{{\text{U}}}\), \(G_{3} = a_{3}^{{\text{U}}} b_{3}^{{\text{U}}}\), \(G_{4} = a_{4}^{{\text{U}}} b_{4}^{{\text{U}}}\), \(H_{q \otimes w}^{{\text{U}}} = \min (H_{q}^{{\text{U}}} ,H_{w}^{{\text{U}}} )\); \(E_{3} = (a_{2}^{{\text{L}}} - a_{1}^{{\text{L}}} )(b_{2}^{{\text{L}}} - b_{1}^{{\text{L}}} )\), \(F_{3} = a_{1}^{{\text{L}}} (b_{2}^{{\text{L}}} - b_{1}^{{\text{L}}} ) + b_{1}^{{\text{L}}} (a_{2}^{{\text{L}}} - a_{1}^{{\text{L}}} )\), \(E_{4} = (a_{3}^{{\text{L}}} - a_{4}^{{\text{L}}} )(b_{3}^{{\text{L}}} - b_{4}^{{\text{L}}} )\),\(F_{4} = a_{4}^{{\text{L}}} (b_{3}^{{\text{L}}} - b_{4}^{{\text{L}}} ) + b_{4}^{{\text{L}}} (a_{3}^{{\text{L}}} - a_{4}^{{\text{L}}} )\), \(Q_{1} = a_{1}^{{\text{L}}} b_{1}^{{\text{L}}}\), \(Q_{2} = a_{2}^{{\text{L}}} b_{2}^{{\text{L}}}\), \(Q_{3} = a_{3}^{{\text{L}}} b_{3}^{{\text{L}}}\), \(Q_{4} = a_{4}^{{\text{L}}} b_{4}^{{\text{L}}}\), \(H_{q \otimes w}^{{\text{L}}} = \min (H_{q}^{{\text{L}}} ,H_{w}^{{\text{L}}} )\).
Moreover, \(\overline{f}_{q \otimes w}^{R} (x) = \frac{{ - F_{2} + \sqrt {F_{2}^{2} - 4E_{2} (G_{4} - x)} }}{{2E_{2} }}\) and \(\underline {f}_{q \otimes w}^{R} (x) = \frac{{ - F_{4} + \sqrt {F_{4}^{2} - 4E_{4} (Q_{4} - x)} }}{{2E_{4} }}\) are not allowed because their grades are not zero when applying \(x = G_{4}\) and \(x = Q_{4}\), respectively.□
Appendix B
Corollary 1
\(\overline{f}_{q \otimes w}^{L} (x) = 0\) if \(x = G_{1}\) and \(\underline {f}_{q \otimes w}^{L} (x) = 0\) if \(x = Q_{1}\).
Proof
Apply \(x = G_{1}\) to \(\overline{f}_{q \otimes w}^{L} (x) = \frac{{ - F_{1} { + }\sqrt {F_{1}^{2} - 4E_{1} (G_{1} - x)} }}{{2E_{1} }}\) to obtain.
Similarly, \(\underline {f}_{q \otimes w}^{L} (x)\) = 0 if \(x = Q_{1}\).□
Corollary 2
\(\overline{f}_{q \otimes w}^{R} (x) = 0\) if \(x = G_{4}\) and \(\underline {f}_{q \otimes w}^{R} (x) = 0\) if \(x = Q_{4}\).
Proof
Apply \(x = G_{4}\) to \(\overline{f}_{q \otimes w}^{R} (x) = \frac{{ - F_{2} - \sqrt {F_{2}^{2} - 4E_{2} (G_{4} - x)} }}{{2E_{2} }}\) to obtain.
Similarly, \(\underline {f}_{q \otimes w}^{R} (x) = 0\) if \(x = Q_{4}\). □
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Le, T.H.P., Chu, TC. A weighted centroids approach based trapezoidal interval type-2 fuzzy TOPSIS method for evaluating agricultural risk management tools. Soft Comput 27, 17153–17173 (2023). https://doi.org/10.1007/s00500-023-08953-9
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DOI: https://doi.org/10.1007/s00500-023-08953-9