A self-testing cloud model for multi-criteria group decision making

Abstract

Purpose

The purpose of this paper is to select the best scaling coefficient during the quantitative-qualitative conversion.

Design/methodology/approach

Cloud model can describe the qualitative concept of randomness and fuzziness, achieve uncertain transition between qualitative and quantitative in the field of multi-criteria group decision and has been receiving widespread attention. This paper discusses scale conversion issues of the cloud model when evaluating qualitative information. In order to improve the accuracy of the evaluation on multi-attribute decision problems based on uncertainty of natural linguistic information, this paper proposes a method of self-testing cloud model based on a composite scale (with the exponential scale and the scale as a basis).

Findings

Through experimental verification results show that under composite scale, the best suitable selection of can effectively improve the accuracy and reliability of decision results.

Originality/value

This research presents a new approach to determine the suitable value for coefficient based on uncertain knowledge of natural multi-criteria group decision making, and gives concrete steps and examples. This method has positive significance to improve the quality of qualitative and quantitative conversion based on cloud model.

Citation

Chang, T.-C. and Wang, H. (2016), "A self-testing cloud model for multi-criteria group decision making", Engineering Computations, Vol. 33 No. 6, pp. 1767-1783. https://doi.org/10.1108/EC-08-2015-0258