Multi-criteria decision making problems with unknown weight information under uncertain evaluations

Abstract

Generally, the overall evaluation of a multi-criteria decision making (MCDM) problem is based on alternatives evaluations over a set of criteria and the weights of the criteria. However, in cases where the criteria weights are unknown, the overall evaluations cannot be derived. Therefore, several methods have been proposed to handle such MCDM problems. Nevertheless, there exist MCDM problems with small amount of data and poor information, which cannot be described by a probability distribution. In such MCDM problems, the applicability of existing approaches would be influenced. Accordingly, this paper investigates this type of MCDM problems with small amount of data and poor information, where information on criteria weights is unknown. To this end, a new hybrid MCDM is proposed; in which the unknown criteria weights are estimated using the maximizing deviation method with grey systems theory’s principles. Consequently, potential alternatives are evaluated and ranked by integrating degrees of possibility and PROMETHEE II. To show the feasibility and practicability of the proposed methodology an example is provided and to validate the methodology, a comparative analysis with an existing approach is performed.