An Assessment of Indifference Threshold Values to Achieve Full Objective Indifference Threshold-Based Attribute Ratio Analysis

Multi-criteria decision-making (MCDM) models are moving toward being data-oriented. Meanwhile, MCDM models’ totalitarian reliance on experts’ preferences may reduce the accuracy of results in real-world challenges. Therefore, there is a huge gap in refining MCDM models to be data-structured rather than relying on experts’ and decision-makers’ ideas. In this research article, the primary indifference threshold values of the Indifference Threshold-based Attribute Ratio Analysis (ITARA) model, which is one of the popular objective weighting MCDM techniques, have been investigated and improved to achieve the goal of a full-objective MCDM model. ITARA utilizes decision-makers’ and experts’ opinions to set the indifference threshold values, which are integral to obtaining criteria weights, and since this step is not data-based, unlike the whole technique, it is prone to deficiencies. Three critical frameworks based on the minimum value, standard deviation, and max–min distance are designed to assess the sensitivity of the indifference threshold values and optimize the initialization values to start the model. Two case studies based on actual data are considered in this research to observe the frameworks’ outcomes and the rank reversal phenomenon. The results demonstrated that the assigning weights procedure is deeply sensitive to a max–min framework, while the standard deviation framework illustrated more stable results and a slight change in criteria rankings. The min framework moderately fluctuated between the max–min and standard deviation frameworks.