Hybrid grey multiple attribute decision-making method with partial weight information

Purpose - The purpose of this paper is to attempt to propose a method based on evidential reasoning for hybrid grey attribute decision-making problems where the attribute weights are partially known, in which the attribute values are interval grey numbers and linguistic grades. Design/methodology/approach - The method is that the decision-maker gives whitenization of each interval grey number based on their preference and belief structure of the form of qualitative attribute values, whitenization of quantitative attributes can be equivalently expressed in the form of belief structure with the principle of utility value equivalence, and then the grade belief structure decision matrix can be determined. By using the analytical evidential reasoning algorithm, belief degrees of each alternative belonging to each linguistic grade are obtained. Two pairs of nonlinear optimization models which are solved by genetic algorithms (GA) are constructed to compute the maximum and the minimum expected utilities of each alternative, respectively. Findings - The results show that decision-maker based on his/her risk preference gives whitenization of each interval grey number and selects corresponding alternative policy. Practical implications - The method exposed in the paper can be used to deal with problems of grey multiple attribute decision making and hybrid grey multiple attribute decision making. Originality/value - The paper succeeds in constructing two pairs of nonlinear optimization models based on the analytical evidential reasoning algorithm which are solved by genetic algorithms and the hybrid grey multiple attribute decision-making approach with partial weight information.