The distances and references based solution approach for multi-criteria decision problems

Today, multi-criteria decision-making techniques are widely used in various industries and areas of life. Due to the unique nature of the decision problems, it is not possible to reach an effective solution with the same multi-criteria decision-making technique in all of the decision problems encountered in these various fields. In the context of the structure of decision problems and the decision-maker's preferences, this study proposes new three techniques, Nearest Solution to References (REF), REF-II, and Distances Based Solution Technique for Decision Problems Including Mixed Type Criteria (KARMA), which are based on references and distances. Also, the Interval and Iterative Preference/Priority Scale is proposed for the scaling and weighting of the decision criteria in the decision problems. The main purpose of REF is to help the decision-maker make the best decision by making use of the references to be determined for each criterion in the decision problem faced. REF differs from other multi-criteria decision-making techniques in the literature in that it determines the reference value as a range, using successor values, and it evaluates the criteria measured using different scales. REF-II does not have the problem of rank reversal and does not require recalculation for existing alternatives in removing or adding alternatives from the problem. On the other hand, KARMA is a multi-criteria decision-making technique that can be used in case the decision-maker cannot determine a reference value or an optimization direction on the criteria and therefore the closest or farthest alternative is preferred. Within the scope of the study, these three techniques were applied to the problem of second-hand automobile selection. Also, by including comparative sensitivity analysis; the similarities and differences of the proposed techniques with other techniques are revealed. The proposed techniques, REF, REF-II, and KARMA, we believe, will add significant contributions to the multi-criteria decision-making literature and can be applied successfully in various fields.