The two-stage utility function with an aspiration to mass data and uncertain linguistic environment in multiple experts multiple criteria decision making

Abstract

Utility function with aspiration is proved to be effective in solving Multiple Experts Multiple Criteria Decision Making (MEMCDM) problems. However, with the development of mass data, the previous utility functions are not as effective as in uncertain linguistic environment or in crisp numbers due to the incompatibility between different data types. To address such incompatibility, this paper proposes a two-stage utility function with aspiration based on closeness degree, which is suitable for mass data and uncertain linguistic environment. In particular, we use distribution to depict mass data, define the closeness degree of distribution, and discuss several types of utility functions in detail. An approach for evaluating the MEMCDM problems is also proposed by using the improved utility function. Finally, an example is given to illustrate the flexibility and applicability of the proposed method to different data types.

Keywords:

• Utility function with aspiration
• mass data
• distribution
• closeness degree
• multiple experts multiple criteria decision making (MEMCDM)

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work presented in this paper is supported by the National Natural Science Foundation of China (No. 71701037), National Natural Science Foundation of Hebei province (No. G2021501004), Fundamental Research Funds for the Central Universities (No. N2123020), and Youth Top-notch Talent Support Program of Hebei province (No. BJ2020211).