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New Generation Computing
Erschienen in: New Generation Computing 2/2021

10.08.2021

An Interval-Valued Intuitionistic Hesitant Fuzzy Methodology and Application

verfasst von: Shailendra Kumar Bharati

Erschienen in: New Generation Computing | Ausgabe 2/2021

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Abstract

Using advantages of interval-valued intuitionistic hesitant fuzzy sets (IVIHFS) for describing the hesitant and intuitionistic decisions of experts and identifying the limitations of previous research works about optimization techniques, this paper introduces a new optimization technique and provides a new computational algorithm, applicable in various real life multiobjective optimization problem (MOOP) of engineering and management sectors, and for this, a new operation between IVIHFSs is first introduced. On the basis of this concept, a stepwise computational algorithm is constructed, and it is an extension of both fuzzy and intuitionistic fuzzy optimization techniques. Finally, the proposed algorithm is illustrated using a production planning problem, and the obtained results are compared with the existing optimization techniques.
Vorheriger Artikel Recurring Drift Detection and Model Selection-Based Ensemble Classification for Data Streams with Unlabeled Data
Nächster Artikel An Agent-Based Model to Study Informational Cascades in Financial Markets

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Literatur
1.
2.
Arya, V., Kumar, S.: Knowledge measure and entropy: a complementary concept in fuzzy theory. Granul. Comput. 6, 631–643 (2021)CrossRef
3.
Atanassov T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20, 87–96 (1986)CrossRef
4.
5.
Bharati, S.K., Singh, S.R.: Solution of multiobjective linear programming problems in interval-valued intuitionistic fuzzy environment. Soft. Comput. 23, 77–84 (2019)CrossRef
6.
Brikaa, M.G., Zheng, Z., Ammar, E.S.: Fuzzy multi-objective programming approach for constrained matrix games with payoffs of fuzzy rough numbers. Symmetry. 11, 702 (2019)CrossRef
7.
Charnes, A., Cooper, W.W.: Goal programming and multiple objective optimizations: Part 1. Eur. J. Oper. Res. 1, 39–54 (1977)CrossRef
8.
Cheng, H., Huang, W., Zhou, Q., Cai, J.: Solving fuzzy multi-objective linear programming problems using deviation degree measures and weighted max-min method. Appl. Math. Model. 37(10–11), 6855–6869 (2013)MathSciNetCrossRef
9.
El Sayed, M. A., Abo-Sinna, M. A.: A novel approach for fully intuitionistic fuzzy multi-objective fractional transportation problem. Alex. Eng. J. 60, 1447–1463 (2021)CrossRef
10.
Evans, J. P., Steuer, R. E.: A revised simplex method for linear multiple objective programs. Math. Program. 5, 54–72 (1973)MathSciNetCrossRef
11.
Freen, G., Kousar, S., Khalil, S.: Multi-objective non-linear four-valued refined neutrosophic optimization. Comp. Appl. Math. 39, 35 (2020)MathSciNetCrossRef
12.
13.
Joshi, D.K., Kumar, S.: Entropy of interval-valued intuitionistic hesitant fuzzy set and its application to group decision making problems. Granul. Comput. 3, 367–381 (2018)CrossRef
14.
Karimi, H., Bahmani, R., Jadid, S.: Stochastic multi-objective optimization to design optimal transactive pricing for dynamic demand response programs: a bi-level fuzzy approach. Int. J. Power Energy Syst. 125, 106487 (2021)CrossRef
15.
Khalil, S., Smarandache, F., Kousar, S., Freen, G.: Multiobjective nonlinear bipolar neutrosophic optimization and its comparison with existing techniques. Optim. Theory Based Neutrosophic Plithogenic Sets 289–314 (2020)
16.
Kumar, P.S.: Finding the solution of balanced and unbalanced intuitionistic fuzzy transportation problems by using different methods with some software packages. In: Handbook of Research on Applied AI for International Business and Marketing Applications, pp. 278–320. IGI Global (2021)
17.
Kumar, P.S.: The PSK method for solving fully intuitionistic fuzzy assignment problems with some software tools. In: Theoretical and Applied Mathematics in International Business, pp. 149–202. IGI Global (2020)
18.
Kumar, P. S.: Intuitionistic fuzzy zero point method for solving type-2 intuitionistic fuzzy transportation problem. Int. J. Oper. Res. 37, 418–451 (2020)MathSciNetCrossRef
19.
Kumar, P. S.: Algorithms for solving the optimization problems using fuzzy and intuitionistic fuzzy set. Int. J. Syst. Assur. Eng. Manag. 11, 189–222 (2020)CrossRef
20.
Kumbhar, K., Das, S.: Solving multi-attribute decision-making problems using probabilistic interval-valued intuitionistic hesitant fuzzy set and particle swarm optimization. Num. Optim. Eng. Sci. 149–158 (2020)
21.
Liu, L., Chen, Y.: Interval-valued intuitionistic hesitant fuzzy Quasi-Choquet geometric operators based TOPSIS method for multi-criteria group decision making, 29th Chinese Control And Decision Conference (CCDC), Chongqing 2374–2380 (2017)
22.
Lu, J., Wu, F., Zhang, G.: On a generalized fuzzy goal optimization for solving fuzzy multi-objective linear programming problems. J. Intell. Fuzzy Syst. 18, 83–97 (2007)MATH
23.
Mahajan, S., Gupta, S.K.: On fully intuitionistic fuzzy multiobjective transportation problems using different membership functions. Ann. Oper. Res. 1–31 (2019)
24.
25.
Narayanamoorthy, S., Geetha, S., Rakkiyappan, R., Joo, Y. H.: Interval-valued intuitionistic hesitant fuzzy entropy based VIKOR method for industrial robots selection. Expert Syst. Appl. 121, 28–37 (2019)CrossRef
26.
Ranjbar, M., Effati, S.: Symmetric and right-hand-side hesitant fuzzy linear programming. IEEE Trans. Fuzzy Syst. 28, 215–227 (2019)CrossRef
27.
28.
29.
Ranjbar, M., Miri, S.M., Effati, S.: Hesitant fuzzy numbers with (a, k)-cuts in compact intervals and applications. Expert Syst Appl. 151, 113363 (2020)CrossRef
30.
Rouhbakhsh, F. F., Ranjbar, M., Effati, S., Hassanpour, H.: Multi objective programming problem in the hesitant fuzzy environment. Appl. Intell. 50, 2991–3006 (2020)CrossRef
31.
Sen, S., Patra, K. Mondal, S.K.: A new approach to similarity measure for generalized trapezoidal fuzzy numbers and its application to fuzzy risk analysis. Granul. Comput. 6, 705–718 (2021)CrossRef
32.
Shih, T. S., Lee, H. M., Su, J. S.: Fuzzy multiple objective programming based on interval-valued fuzzy sets. Eighth Int. Conf. Intell. Syst. Design Appl. 1, 397–402 (2008)
33.
Sooraj, T.R., Mohanty, R.K., Tripathy, B.K.: A new approach to interval-valued intuitionistic hesitant fuzzy soft sets and their application in decision making. In: Satapathy, S., Bhateja, V., Das, S. (eds.) Smart computing and informatics. Smart innovation, systems and technologies, vol. 77. Springer, Singapore (2018)
34.
Tarabia, A.M.K., Kassem, M.A.E. El-Badry, N.M.: A modified approach for solving a fuzzy multi-objective programming problem. Appl. Inform. 4, 1 (2017)CrossRef
35.
Torra V.: Hesitant fuzzy sets. Int. J. Intell. Syst. 25, 529–539 (2010)MATH
36.
Uddin, M. S., Miah, M., Khan, M. A. A., AlArjani, A.: Goal programming tactic for uncertain multi-objective transportation problem using fuzzy linear membership function. Alex. Eng. 60, 2525–2533 (2021)CrossRef
37.
Vij, S., Jain, A., Tayal, D., Castillo, O.: Scientometric inspection of research progression in hesitant fuzzy sets. J. Intell. Fuzzy Syst. 38, 619–626 (2020)CrossRef
38.
Wei, Y., Gao, L., Wang, C., Ha, M.: Distance measures for interval-valued intuitionistic hesitant fuzzy sets. In: Cao, B.Y., Liu, Z.L., Zhong, Y.B., Mi, H.H. (eds) Fuzzy systems and operations research and management. Advances in intelligent systems and computing, vol. 367. Springer, Cham (2016). https://​doi.​org/​10.​1007/​978-3-319-19105-8_​4
39.
Wu, F., Lu, J., Zhang, G.: A new approximate algorithm for solving multiple objective linear programming problems with fuzzy parameters. Appl. Math. Comput. 174, 524–544 (2006)MathSciNetMATH
40.
Xia, M.M., Xu, Z.S.: Studies on the aggregation of intuitionistic fuzzy and hesitant fuzzy information. Tech. Rep. (2011)
41.
Xia, M.M., Xu, Z.S.: Hesitant fuzzy information aggregation in decision making. Int. J. Approx. Reason. 52, 395–407 (2011)MathSciNetCrossRef
42.
Yang, G., Li, X., Huo, L., Liu, Q.: A solving approach for fuzzy multi-objective linear fractional programming and application to an agricultural planting structure optimization problem. Chaos Solitons Fractals 141, 110352 (2020)MathSciNetCrossRef
43.
44.
45.
46.
47.
48.
Metadaten
Titel
An Interval-Valued Intuitionistic Hesitant Fuzzy Methodology and Application
verfasst von
Shailendra Kumar Bharati
Publikationsdatum
10.08.2021
Verlag
Ohmsha
Erschienen in
New Generation Computing / Ausgabe 2/2021
Print ISSN: 0288-3635
Elektronische ISSN: 1882-7055
DOI
https://doi.org/10.1007/s00354-021-00132-4

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