Two-person game with hesitant fuzzy payoff: An application in MADM
RAIRO. Operations Research, Tome 55 (2021) no. 5, pp. 3087-3105

Hesitant Fuzzy Set (HFS) permits the membership function having a collection of probable values which are more effective for modelling the real-life problems. Multiple Attribute Decision Making (MADM) process apparently assesses multiple conflicting attribute in decision making. In traditional decision making problems, each player is moving independently whereas in reality it is seen that each player aims to maximize personal profit which causes a negative impact on other player. MADM problem treats with candidate to the best alternative corresponding to the several attributes. Here, we present an MADM problem under hesitant fuzzy information and then transforming it into two-person matrix game, referred to herein as MADM game. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is one of the prominent approach for solving the MADM problems. In this work, we develop the TOPSIS based on Ordered Weighted Aggregation (OWA) operator and hybrid hesitant fuzzy normalized Euclidean distance.Please check whether short title on odd pages have been set correctly. Then the two approaches, namely Hybrid Hesitant Fuzzy Ordered Weighted Aggregation-TOPSIS (HHFOWA-TOPSIS) and the Linear Programming Problem (LPP) are applied to solve the formulated MADM game. For solving MADM game, we apply LPP by considering the various values of α,ψ, and HHFOWA-TOPSIS for finding the optimal alternative according to their scores.Please provide missing AMS classification codes. An investment selection problem is included to explain the feasibility and superiority of our formulated approaches. A comparison analysis is drawn among the obtained results which are derived from the two approaches. LPP and HHFOWA-TOPSIS provide the best alternative for the proposed problem. Finally, conclusions about our findings and outlooks are described.

DOI : 10.1051/ro/2021149
Classification : 91A05, 91A35, 03E72, 06D72, 62C86
Keywords: Matrix games, multiple attribute decision making, hesitant fuzzy set, aggregation operator, HHFOWA-TOPSIS
@article{RO_2021__55_5_3087_0,
     author = {Jana, Jishu and Roy, Sankar Kumar},
     title = {Two-person game with hesitant fuzzy payoff: {An} application in {MADM}},
     journal = {RAIRO. Operations Research},
     pages = {3087--3105},
     year = {2021},
     publisher = {EDP-Sciences},
     volume = {55},
     number = {5},
     doi = {10.1051/ro/2021149},
     mrnumber = {4324003},
     language = {en},
     url = {https://www.numdam.org/articles/10.1051/ro/2021149/}
}
TY  - JOUR
AU  - Jana, Jishu
AU  - Roy, Sankar Kumar
TI  - Two-person game with hesitant fuzzy payoff: An application in MADM
JO  - RAIRO. Operations Research
PY  - 2021
SP  - 3087
EP  - 3105
VL  - 55
IS  - 5
PB  - EDP-Sciences
UR  - https://www.numdam.org/articles/10.1051/ro/2021149/
DO  - 10.1051/ro/2021149
LA  - en
ID  - RO_2021__55_5_3087_0
ER  - 
%0 Journal Article
%A Jana, Jishu
%A Roy, Sankar Kumar
%T Two-person game with hesitant fuzzy payoff: An application in MADM
%J RAIRO. Operations Research
%D 2021
%P 3087-3105
%V 55
%N 5
%I EDP-Sciences
%U https://www.numdam.org/articles/10.1051/ro/2021149/
%R 10.1051/ro/2021149
%G en
%F RO_2021__55_5_3087_0
Jana, Jishu; Roy, Sankar Kumar. Two-person game with hesitant fuzzy payoff: An application in MADM. RAIRO. Operations Research, Tome 55 (2021) no. 5, pp. 3087-3105. doi: 10.1051/ro/2021149

[1] F. Achemine, A. Merakeb, M. Larbani and P. Marthon, Z-equilibria in bi-matrix games with uncertain payoffs. RAIRO Oper. Res. 54 (2020) 393–412. | MR | Zbl | Numdam | DOI

[2] M. Aggarwal, Hesitant information sets and application in group decision making. Appl. Soft Comput. 75 (2019) 120–129. | DOI

[3] A. Aggarwal and I. Khan, Solving multi-objective fuzzy matrix games via multi-objective linear programming approach. Kybernetika 52 (2016) 153–168. | MR

[4] C. R. Bector, S. Chandra and V. Vijay, Duality in linear programming with fuzzy parameters and matrix games with fuzzy payoffs. Fuzzy Sets Syst. 46 (2004) 253–269. | Zbl | MR | DOI

[5] A. Bhaumik and S. K. Roy, Intuitionistic interval-valued hesitant fuzzy matrix games with a new aggregation operator for solving management problem. Granul. Comput. 6 (2021) 359–375. | DOI

[6] A. Bhaumik, S. K. Roy and D. F. Li, Analysis of triangular intuitionistic fuzzy matrix games using robust ranking. J. Intell. Fuzzy Syst. 33 (2017) 327–336. | DOI

[7] A. Bhaumik, S. K. Roy and G. W. Weber, Hesitant interval-valued intuitionistic fuzzy-linguistic term set approach in Prisoners’ dilemma game theory using TOPSIS: a case study on Human-trafficking. Cent. Eur. J. Oper. Res. 28 (2020) 797–816. | MR | DOI

[8] H. Bigdeli and H. Hassanpour, An approach to solve multi-objective linear production planning games with fuzzy parameters. Yugosl. J. Oper. Res. 28 (2018) 237–248. | MR | DOI

[9] L. Campos, Fuzzy linear programming models to solve fuzzy matrix games. Fuzzy Sets Syst. 32 (1989) 275–289. | Zbl | MR | DOI

[10] S. J. Chen and C. L. Hwang, Fuzzy Multiple Attribute Decision Making: Methods and Applications. Springer, New York (1992). | Zbl | MR | DOI

[11] Y. W. Chen and M. Larbani, Two-person zero-sum game approach for fuzzy multiple attribute decision making problems. Fuzzy Sets Syst. 157 (2006) 34–51. | Zbl | MR | DOI

[12] P. E. Ezimadu, Modelling cooperative advertising decisions in a manufacturer-distributor-retailer supply chain using game theory. Yugosl. J. Oper. Res. 30 (2020) 147–176. | MR | DOI

[13] B. Farhadinia and Z. S. Xu, A novel distance-based multiple attribute decision-making with hesitant fuzzy sets. Soft Comput. 24 (2020) 5005–5017. | DOI

[14] A. Hatami-Marbini and F. Kangi, An extension of fuzzy TOPSIS for a group decision making with an application to Tehran stock exchange. Appl. Soft Comput. 52 (2017) 1084–1097. | DOI

[15] C. L. Hwang and K. Yoon, Multiple attribute decision making methods and applications. Springer-Verlag, New York (1981). | Zbl | MR | DOI

[16] J. Jana and S. K. Roy, Solution of matrix games with generalized trapezoidal fuzzy payoffs. Fuzzy Inf. Eng. 10 (2018) 213–224. | DOI

[17] J. Jana and S. K. Roy, Dual hesitant fuzzy matrix games: based on new similarity measure. Soft Comput. 23 (2019) 8873–8886. | DOI

[18] J. Jana and S. K. Roy, Soft matrix game: A hesitant fuzzy MCDM approach. Am. J. Math. Manag. Sci. 40 (2021) 107–119.

[19] L. S. Jin, R. Mesiar and R. Yager, Ordered weighted averaging aggregation on convex poset. IEEE Trans. Fuzzy Syst. 27 (2019) 612–617. | DOI

[20] S. Khalilpourazari and H. H. Doulabi, Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec. Ann. Oper. Res. (2021) 1–45. DOI: . | DOI | MR

[21] S. Khalilpourazari and H. H. Doulabi, Robust modelling and prediction of the COVID-19 pandemic in Canada. Int. J. Prod. Res. (2021) 1–17. DOI: . | DOI

[22] S. Khalilpourazari and S. H. R. Pasandideh, Designing emergency flood evacuation plans using robust optimization and artificial intelligence. J. Comb. Optim. 41 (2021) 640–677. | MR | DOI

[23] S. Khalilpourazari, H. H. Doulabi, A. O. Çiftçioğlu and G. W. Weber, Gradient-based grey wolf optimizer with Gaussian walk: Application in modelling and prediction of the COVID-19 pandemic. Expert Syst. Appl. 177 (2021). DOI: . | DOI

[24] S. Lalotra and S. Singh, Knowledge measure of hesitant fuzzy set and its application in multi-attribute decision-making. Comput. Appl. Math. 39 (2020) 1–31. | MR | DOI

[25] M. Larbani, Solving bimatrix games with fuzzy payoffs by introducing nature as a third player. Fuzzy Sets Syst. 160 (2009) 657–666. | Zbl | MR | DOI

[26] M. Larbani, Non cooperative fuzzy games in normal form: A survey. Fuzzy Sets Syst. 160 (2009) 3184–3210. | Zbl | MR | DOI

[27] D. Liang and Z. S. Xu, The new extension of TOPSIS method for multiple criteria decision making with hesitant pythagorean fuzzy sets. Appl. Soft Comput. 60 (2017) 167–179. | DOI

[28] H. Liao and Z. S. Xu, Some new hybrid weighted aggregation operators under hesitant fuzzy multi-criteria decision making environment. J. Intell. Fuzzy Syst. 26 (2014) 1601–1617. | Zbl | MR | DOI

[29] R. Lotfi, M. Nayeri, S. Sajadifar and N. Mardani, Determination of start times and ordering plans for two-period projects with interdependent demand in project-oriented organizations: A case study on molding industry. J. Proj. Manag. 2 (2017) 119–142.

[30] R. Lotfi, Z. Yadegari, S. H. Hosseini, A. H. Khameneh, E. B. Tirkolaee and G. W. Weber, A robust time-cost-quality-energy-environment trade-off with resource-constrained in project management: A case study for a bridge construction project. J. Ind. Manag. Optim. 13 (2020) 1–22. | MR

[31] R. Lotfi, N. Mardani and G. W. Weber, Robust bi-level programming for renewable energy location. Int. J. Energy Res. 45 (2021) 7521–7534. | DOI

[32] R. Lotfi, B. Kargar, S. H. Hoseini, S. Nazari, S. Safavi and G. W. Weber, Resilience and sustainable supply chain network design by considering renewable energy. Int. J. Energy Res. (2021). DOI: . | DOI

[33] J. M. Merigó, A unified model between the weighted average and induced OWA operator. Expert Syst. Appl. 38 (2011) 11560–11572. | DOI

[34] X. Mo, H. Zhao and Z. S. Xu, Feature-based hesitant fuzzy aggregation method for satisfaction with life scale. Appl. Soft Comput. 94 (2020). DOI: . | DOI

[35] P. Mula, S. K. Roy and D. F. Li, Birough programming approach for solving bi-matrix games with birough payoff elements. J. Intell. Fuzzy Syst. 29 (2015) 863–875. | MR | DOI

[36] I. Nishizaki and M. Sakawa, Fuzzy and multiobjective games for conflict resolution. Physica-Verlag, Heidelberg (2001). | Zbl | MR | DOI

[37] T. Parthasarathy and T. E. S. Raghavan, Some topics in two-person games. American Elsevier Publishing Company, New York (1971). | Zbl | MR

[38] S. K. Roy, Game theory under MCDM and fuzzy set theory, VDM. VDM (Verlag Dr. Muller), Germany (2010).

[39] S. K. Roy and P. Mula, Bimatrix game in bifuzzy rough environment. J. Uncertainty Anal. Appl. 1 (2013) 11. | DOI

[40] S. K. Roy and P. Mula, Rough set approach to bimatrix game. Int. J. Oper. Res. 23 (2015) 229–244. | MR | DOI

[41] S. K. Roy and S. N. Mondal, An approach to solve fuzzy interval valued matrix game. Int. J. Oper. Res. 26 (2016) 253–267. | MR

[42] S. K. Roy and P. Mula, Solving matrix game with rough payoffs using genetic algorithm. Oper. Res. Int. J. 16 (2016) 117–130. | DOI

[43] S. K. Roy and A. Bhaumik, Intelligent water management: a triangular type-2 intuitionistic fuzzy matrix games approach. Water Resour. Manag. 32 (2018) 949–968. | DOI

[44] S. K. Roy and J. Jana, The multi-objective linear production planning games in triangular hesitant fuzzy sets. Sadhana 46 (2021) 176. | MR | DOI

[45] M. Sakawa and H. Yano, Interactive decision making for multiobjective non linear programming problems with fuzzy parameters. Fuzzy Sets Syst. 29 (1989) 315–326. | Zbl | MR | DOI

[46] M. Sakawa and I. Nishizaki, Max-min solution for fuzzy multiobjective matrix games. Fuzzy Sets Syst. 67 (1994) 53–69. | Zbl | MR | DOI

[47] S. Singh and S. Lalotra, On generalized correlation coefficients of the hesitant fuzzy sets with their application to clustering analysis. Comput. Appl. Math. 38 (2019) 1–26. | MR | DOI

[48] G. Sun, X. Guan, X. Yi and Z. Zhou, An innovative TOPSIS approach based on hesitant fuzzy correlation coefficient and its applications. Appl. Soft Comput. 68 (2018) 249–267. | DOI

[49] V. Torra and Y. Narukawa, On hesitant fuzzy sets and decision, In: Proceedings of the IEEE International Conference on Fuzzy Systems, Jeju Island, Korea (2009) 1378–1382.

[50] C. Y. Wang and S. M. Chen, Multiple attribute decision making based on interval-valued intuitionistic fuzzy sets, linear programming methodology, and the extended TOPSIS method. Inf. Sci. 397 (2017) 155–167. | DOI

[51] M. M. Xia and Z. S. Xu, Hesitant fuzzy information aggregation in decision making. Int. J. Approx. Reason. 52 (2011) 395–407. | Zbl | MR | DOI

[52] S. H. Xiong, Z. S. Chen and K. S. Chin, A novel MAGDM approach with proportional hesitant fuzzy sets. Int. J. Comput. Intell. Syst. 11 (2018) 256–271. | DOI

[53] R. R. Yager, On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans. Syst. Man Cybern. 18 (1988) 183–190. | Zbl | MR | DOI

[54] K. P. Yoon and W. K. Kim, The behavioral TOPSIS. Expert Syst. Appl. 89 (2017) 266–272. | DOI

[55] L. A. Zadeh, Fuzzy sets. Inf. Control. 8 (1965) 338–356. | Zbl | MR | DOI

Cité par Sources :