Abstract
The paper aims are to present a method to solve the multiple-attribute decision-making (MADM) problems under the hesitant fuzzy set environment. In MADM problems, the information collection, aggregation, and the measure phases are crucial to direct the problem. However, to handle the uncertainties in the collection data, a hesitant fuzzy number is one of the most prominent ways to express uncertain and vague information in terms of different discrete numbers rather than a single crisp number. Additionally, to aggregate and to rank the collective numbers, a TOPSIS (“Technique for Order of Preference by Similarity to Ideal Solution”) and the Choquet integral (CI) are the useful tools. Keeping all these features, in the present paper, we combine the TOPSIS and CI methods for hesitant fuzzy information and hence present a method named as TOPSIS-CI to address the MADM problems. The presented method has been described with a numerical example. Finally, the validity of the stated method as well as a comparative analysis with the existing methods is addressed in detail.
Highlights
Decision-making plays a vital role in the practical life activities of human beings as it refers to a process that lays out all the options according to the assessment data of the decision makers and selects the excellent one, mostly happening in our everyday lives
In an hesitant fuzzy set (HFS), the set of membership degrees assigned to each member is called a hesitant fuzzy element (HFE)
Torra and Narukawa [8] presented a relationship between the HFS and other extended FSs and found that the intuitionistic fuzzy set [9] appeared to be an envelope of HFS
Summary
Decision-making plays a vital role in the practical life activities of human beings as it refers to a process that lays out all the options according to the assessment data of the decision makers and selects the excellent one, mostly happening in our everyday lives. Liao and Xu [33] developed a VIKOR method for solving MADM under HFS environment He [34] presented a Dombi t-norm-based operational laws for HFSs and their corresponding AOs for solving the MADM problems. Tan et al [42] presented Hamacher t-norm-based operational rules for HFSs. From the above investigation, it has been observed that the HFS is a powerful tool to solve the decision-making problems and widely used in the literature. Deli [25] presented a generalized trapezoidal hesitant fuzzy (GTHF) numbers and introduced some distance measures including Hamming, Euclidean, and Hausdorff Based on these measures, a TOPSIS approach is presented to solve the MADM problems of GTHF numbers.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
