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

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.

Automated summary

This paper introduces a two-stage utility function with aspiration based on closeness degree, which is suitable for solving Multiple Experts Multiple Criteria Decision Making (MEMCDM) problems in mass data and uncertain linguistic environment. The paper extensively discusses the closeness degree of distribution, various types of utility functions, and proposes an approach for evaluating MEMCDM problems.