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The hesitant fuzzy set (HFS) concept is a highly effective tool for dealing with uncertain data. Indeed, the latter approach allows for the representation of an attribute's membership degree in a given set as a range of possible numerical values between [0,1]. In practise, the length of the hesitant fuzzy element varies. Certain methods include adding elements to a shorter hesitant fuzzy element, equating it to another hesitant fuzzy element, or repeating their elements in order to obtain two series of equal length. Clearly, these methods will destroy the original data structure and modify the data. To cater this problem, we proposed a new distance method in this paper based on the score function (arithmetic-mean, geometric-mean, product, and fractional) in the ideal solution of technique for order of preference by similarity to ideal solution (TOPSIS) in the HFS environment. Then, we apply the proposed method of distance score function for selecting an investment portfolio in multi-criteria decision making (MCDM) problem. Finally, a numerical example of investment portfolio decision making in a hesitant fuzzy environment is used to demonstrate their benefits and feasibility. To validate the proposed approach, a comparison with other methods is presented; the results are consistent, demonstrating that this technique is faster and more effective in practical applications.
Beg, I., & Rashid, T. (2016). Ideal solutions for hesitant fuzzy soft sets. Journal of Intelligent and Fuzzy Systems 30(1): 143–150. doi:10.3233/IFS-151740
Beg, I. & Rashid, T. (2017). Modelling Uncertainties in Multi-Criteria Decision Making using Distance Measure and TOPSIS for Hesitant Fuzzy Sets. Journal of Artificial Intelligence and Soft Computing Research 7(2): 103–109. doi:10.1515/jaiscr-2017-0007
Ece, O., & Uludag, A. S. (2017). Applicability of Fuzzy TOPSIS Method in Optimal Portfolio Selection and an Application in BIST. International Journal of Economics and Finance 9(10): 107. doi:10.5539/ijef.v9n10p107
Farhadinia, B. (2014). A series of score functions for hesitant fuzzy sets. Information Sciences 277: 102–110. doi:10.1016/j.ins.2014.02.009
Garmendia, L., González del Campo, R., & Recasens, J. (2017). Partial orderings for hesitant fuzzy sets. International Journal of Approximate Reasoning 84: 159–167. doi:10.1016/j.ijar.2017.02.008
Huang, Y., & Jiang, W. (2018). Extension of TOPSIS Method and its Application in Investment. Arabian Journal for Science and Engineering 43(2): 693–705. doi:10.1007/s13369-017-2736-3
Hwang, C.-L., & Yoon, K. (1981). Multiple Attribute Decision Making — Methods and Applications. doi:10.1007/978-3-642-45511-7
Mishra, A. R., Rani, P., & Pardasani, K. R. (2019). Multiple-criteria decision-making for service quality selection based on Shapley COPRAS method under hesitant fuzzy sets. Granular Computing 4(3): 435–449. doi:10.1007/s41066-018-0103-8
Moayyed, F. M., Semiari, M., Hamzeloei, S., & Semiari, M. (2019). Identifying the Factors Affecting Manufacturing Investment Projects and Using TOPSIS Method for Prioritizing Projects 3(12): 51–61.
Ozyesil, M. (2019). An Application of TOPSIS Method for Financial Decision Making Process?: A Research on Real Estate Investment Trusts Listed in Borsa Istanbul AN APPLICATION OF TOPSIS METHOD FOR FINANCIAL DECISION MAKING PROCESS?: A RESEARCH ON REAL ESTATE INVESTMENT TRUST. Journal of International Trade, Logistics and Law 5(2): 70–80.
Rodzi, Z., & Ahmad, A. G. (2020). Fuzzy Parameterized Hesitant Fuzzy Linguistic Term Soft Sets ( FPHFLTSSs ) in Multi-Criteria Decision Making. International Journal of Innovative Technology and Exploring Engineering (IJITEE) 9(5): 909–916. doi:10.35940/ijitee.E2519.039520
Torra, V. (2010). Hesitant Fuzzy Sets. international Journal of Intelligent system 25: 529–539. doi:10.1002/int
Torra, V., & Narukawa, Y. (2009). On hesitant fuzzy sets and decision. IEEE International Conference on Fuzzy Systems 1378–1382. doi:10.1109/FUZZY.2009.5276884
Xia, M., & Xu, Z. (2011). Hesitant fuzzy information aggregation in decision making. International Journal of Approximate Reasoning 52(3): 395–407. doi:10.1016/j.ijar.2010.09.002
Xu, Z., & Xia, M. (2011). Distance and similarity measures for hesitant fuzzy sets. Information Sciences 181(11): 2128–2138. doi:10.1016/j.ins.2011.01.028
Xu, Z., & Zhang, X. (2015). Erratum: Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information (Knowledge-Based Systems (2013) 52 (53-64)). Knowledge-Based Systems 77: 128. doi:10.1016/j.knosys.2015.01.012
In-Text Citation: (Radzib et al., 2021)
To Cite this Article: Radzib, P. A. M., Razali, S. S., Sarudin, A. F., & Rodzi, Z. M. (2021). The TOPSIS with Distance Score Function of Hesitant Fuzzy Sets in Investment Selection. International Journal of Academic Research in Accounting Finance and Management Sciences, 11(2), 150-.
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