## 2 Background of the Problem and Research Methodology
### Background of the Problem and Literature Review  
The MCDM framework encompasses a wide variety of methods, each tailored to different decision-making contexts and each with its unique approach and specific algorithms [2; 3; 18; 19]. However, classical MCDM models often struggle to handle uncertainty due to incomplete or imprecise information, which is why Fuzzy Multi-Criteria Decision-Making (FMCDM) methods are highly relevant. These methods handle uncertainties and vague information by incorporating fuzzy logic, which captures the imprecision inherent in human judgment [18; 20]. Intuitionistic fuzzy sets allow for the consideration of hesitation or uncertainty in the decision-making process [21], while interval-valued fuzzy sets provide a way to represent uncertainty more comprehensively by considering ranges of possible values rather than fixed points [22]. Fuzzy logic facilitates the representation of uncertain or ambiguous information through the use of linguistic variables such as \"high\", \"medium\", or \"low\" [23; 24]. This approach is particularly advantageous in decision-making processes where obtaining precise data may be challenging or unattainable. MCDM and FMCDM methods have found extensive applications across various domains [25], including business [26], engineering [27], environmental science [28], economics [29], circular economy [30], finance [31], service quality [32], sustainability [33; 34], bioenergy systems [35], healthcare [36], waste management [37], and transport [38], among others.  
[PERSON] et al. [39] identified three main approaches in multi-criteria analysis: utility function, outranking relation, and sets of decision rules. The utility-based theory includes methods for synthesizing information into a singular parameter to facilitate the ranking of alternatives [40]. This category encompasses techniques such as SMART (Simple Multi-Attribute Rating Technique) [41], SAW (Simple Additive Weighting) [42], TOPSIS [43], MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) [44] and AHP (Analytic Hierarchy Process) [45], among others. The outranking relation theory involves methods that compare pairs of options to assess whether \"alternative A is at least as good as alternative B\" [3]. Key techniques that utilize a preference relational system to compare alternatives and establish preference relations include ELECTRE (Elimination and Choice Expressing Reality) [46] and PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) [47]. Finally, the decision rule approach enables the development of a preference model by classifying or comparing decision examples [48].  
A recent and growing application of MCDM is in the construction of composite indicators. As indicated by [PERSON] et al. [49], the objective of constructing a synthetic measure is \"to condense and summarize the information contained in a number of underlying indicators, in a way that accurately reflects the underlying concept\". A literature review by [PERSON] et al. [50] highlights the substantial expansion of this research field, emphasizing its relevance and practical significance within the realm of MCDM. Within these methods,there is a class of techniques based on aggregation formulas that incorporate reference solutions. These include TOPSIS [43], VIKOR (ViseKriterijumska Optimizacija I Kompromisno Resenje) [51], DARP (Distances to Aspiration Reference Points) method [52], BWM (Best-Worst Method) [53], BIPOLAR [54], and the Taxonomic Measure of Development (TMD) [4].