## 2 Background of the Problem and Research Methodology
### Research Methodology  
The following steps can be applied to the class of methods based on the utility theory approach, with specific details varying depending on the selected MCDM technique. The same principles also apply to TMD and their extensions. In the realm of MCDM, various methods grounded in utility theory provide structured frameworks for analyzing complex decision problems. The specific details of these methods may differ based on the selected technique, yet they all share fundamental principles that guide their implementation. This is also true for the Taxonomic Measure of Development and its extensions.  
The general process for algorithms based on the TMD begins with a crucial first step: data collection. This phase entails gathering relevant information for each alternative in accordance with predefined criteria, setting the stage for subsequent analysis and decision-making. This data can be qualitative or quantitative and should be sourced from reliable references or expert opinions, represented as real numbers, fuzzy or intuitionistic fuzzy numbers, or in linguistic terms [2; 20; 55]. Next, weights are determined where the significance of each criterion in the decision-making process is assessed. This can be undertaken using methods ranging from objective to subjective, or a combination of both [56; 57; 58; 59]. Subsequently, the ideal solution is defined as the ideal value for each criterion. These values, which may be maximum, minimum, or target values, are set according to the decision maker's preferences and are used to evaluate how well each alternative meets the criteria [52; 60]. Similarly, the anti-ideal solution is determined if needed. Following this, normalization is applied to standardize the collected data, ensuring that all criteria are on a common scale. This step employs methods such as standardization, min-max, vector, or linear normalization [61; 62; 63].  
Next, the distance between the normalized values and the ideal values, anti-ideal values, or both of them is calculated. Various metrics can be used for this purpose, including the Euclidean, Manhattan, and Mahalanobis distances or other measures. Afterward, aggregation combines the normalized values, weights, and distance measures to determine an overall preference value for each alternative. This step involves using an aggregation formula, such as a weighted sum, weighted product, or another method depending on the specific MCDM technique employed. Following this step, normalization aggregation standardizes the multi-criteria measure to a common scale, typically within the interval \([0,1]\). Finally, ranking alternatives orders the alternatives based on their relative closeness values, where higher values indicate better alternatives.  
After the original [PERSON]'s method was introduced, various modifications and extensions were proposed to address different conditions. These adaptations focus on aspects such as the type of data, ideal construction, weight system, normalization factor, distance measures, and aggregation formulas. For example, to address uncertainty, a fuzzy [PERSON]'s approach was developed [6; 10; 11], and more recent developments have included intuitionistic approaches [12; 13; 14; 15]. To provide a comprehensive review of techniques based on [PERSON]'s method and their practical applications, Figure 1 will be employed.  
Figure 1: The framework of a comprehensive review of Hellwig-based methods.