Abstract
A linguistic cubic hesitant variable (LCHV) is a hybrid form of linguistic values in group decision-making environments. It is composed of an interval language variable and multiple single-valued language variables given by different decision-makers (DMs). Due to the uncertainty and hesitation of DMs, the numbers of language variables in different LCHVs are unequal. Thus, the least common multiple number (LCMN) extension method was adopted. Based on the included angle and distance of two LCHVs, we presented two cosine similarity measures and developed a multiple attribute group decision-making (MAGDM) approach. An example of engineer selection was used to implement the proposed LCHV MAGDM method and demonstrate the simplicity and feasibility of the proposed method. The sensitivity analysis of weight changes for the two measures showed that the similarity measure based on distance was more stable than the similarity measure based on included angle in this application.
Highlights
In the age of big data, we use a large amount of data information to solve decision-making (DM) problems in various fields, such as manufacturing domain [1], selection of power generation technology [2], the selection of a transport service provider [3], etc
In some uncertain environments it is difficult for decision-makers to make an assessment in a single-valued language variables (LVs); they prefer to give the evaluation in an interval language variable rather than a single-value language variable
Based on the existing operators proposed by Ye [18] and the cosine similarity measures proposed in this paper, the multiple attribute group decision-making (MAGDM) results for the example 1 are shown in the Table 1
Summary
In the age of big data, we use a large amount of data information to solve decision-making (DM) problems in various fields, such as manufacturing domain [1], selection of power generation technology [2], the selection of a transport service provider [3], etc. Dombi weighted arithmetic average (LCVDWAA) operator [17] Based on these operators, scholars developed corresponding approaches to solve MAGDM problems under a LCV environment. LCV can express group decision-making information, the LCV cannot express the evaluation values of the group objectively and accurately when the evaluation values given by decision-makers differ greatly. For this reason, Ye [18] put forward another hybrid form of linguistic values and defined it as a linguistic cubic hesitant variable (LCHV). Presented the LCVWGA and LCVWAA aggregation operators, but the methods were implemented in a given weight. We conclude the study and put forward future prospects for research
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
