Dealing with uncertainty is a difficult task and different tools have been proposed in the literature to handle it. Hesitant fuzzy sets are highly useful in resolving situations where people hesitate when providing their preferences. In this paper, the concept of a hesitant fuzzy soft set is modified to manage the situations in which experts assess an alternative according to finite criteria in all possible values. Next a distance measure is defined between any two elements of hesitant fuzzy soft set. Technique for order preference by similarity to ideal solution is also proposed in hesitant fuzzy soft set. An example is constructed for ranking of alternatives.
AshtianiB., HaghighiradF., MakuiA. and MontazerG., Extension of fuzzy TOPSIS method based on interval-valued fuzzy sets, Applied Soft Computing9(2) (2009), 457–461.
2.
BegI. and RashidT., TOPSIS for hesitant fuzzy linguistic term sets, International Journal of Intelligent Systems28 (2013), 1162–1171.
3.
BegI. and RashidT., Multi-criteria trapezoidal valued intuitionistic fuzzy decision making with Choquet integral based TOPSIS, OPSEARCH51(1) (2014), 98–129.
4.
BellmanR.E. and ZadehL.A., Decision making in a fuzzy environment, Management Science17(4) (1970), 141–164.
5.
CavallaroF., Fuzzy TOPSIS approach for assessing thermalenergy storage in concentrated solar power (CSP) systems, Applied Energy87(2) (2010), 496–503.
6.
ChamodrakasI. and MartakosD., A utility-based fuzzy TOPSIS method for energy efficient network selection in heterogeneous wireless networks, Applied Soft Computing11(4) (2011), 3734–3743.
ChuT-C and LinY-C, An interval arithmetic based fuzzy TOPSIS model, Expert Systems with Applications36 (2009), 10870–10876.
9.
HwangC.L. and YoonK., Multiple attributes decision making methods and applications, Berlin, Heidelberg: Springer, 1981.
10.
KayaT. and KahramanC., Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology, Expert Systems with Applications38(6) (2011), 6577–6585.
11.
KimG., ParkC. and YoonK., Identifying investment opportunities for advanced manufacturing systems with comparativeintegrated, International Journal of Production Economics50 (1997), 23–33.
12.
MahdaviI. and Mahdavi-AmiriN., A Heidarzade and R Nourifar, Designing a model of fuzzy TOPSIS in multiple criteria decision making, Applied Mathematics and Computation206 (2008), 607–617.
13.
NegiD.S., Fuzzy analysis and optimization, PhD thesis. Department of Industrial Engineering, Kansas State University, 1989.
14.
RashidT., BegI. and HusnineS.M., Robot selection by using generalized interval-valued fuzzy numbers with TOPSIS, Applied Soft Computing21 (2014), 462–468.
15.
ShihH., ShyurH. and LeeE., An extension of TOPSIS for group decision making, Mathematical and Computer Modelling45 (2007), 801–813.
16.
TorraV., Hesitant fuzzy sets, International Journal of Intelligent Systems25(6) (2010), 529–539.
17.
WangY.J. and LeeH.S., Generalizing TOPSIS for fuzzy multiple-criteria group decision-making, Computers and Mathematics with Applications53 (2007), 1762–1772.
18.
WangF., LiX. and ChenX., Hesitant fuzzy soft set and its applications in multicriteria decision making, Journal of Applied Mathematics (2014), 10. Article ID 643785.
19.
XiaM. and XuZ., Hesitant fuzzy information aggregation in decision making, International Journal of Approximate Reasoning52 (2011), 395–407.
20.
XuZ. and XiaM., On distance and correlation measures of hesitant fuzzy information, International Journal of Intelligent Systems26 (2011), 410–425.
21.
YanG., LingZ. and DequnZ., Performance evaluation of coal enterprises energy conservation and reduction of pollutant emissions base on GRD-TOPSIS, Energy Procedia5 (2011), 535–539.
