## 3 Materials and Methods
### EDAS Method  
The Evaluation based on Distance from Average Solution (EDAS) method was proposed by [PERSON] et al. [107]. The method defines the desirability of the alternative by examining the distance from the average solution, considering both positive and negative deviations, contrasting with earlier methods based on compromise ranking, such as VIKOR (Visekriterijumsko Kompromisno Rangiranje) and TOPSIS, which measure deviations from an ideal solution. The EDAS method integrates average solutions by addressing the intangibility of decision-makers and the uncertainty inherent in the decision-making context. Additionally, it might prove useful for evaluating conflicting attributes [108]. \begin{table}
\begin{tabular}{c c c} \hline
**Improvement Domains** & **Used Concept** & **References** \\ \hline \hline  & Picture fuzzy & [85] \\ \hline HFLTS & [86] \\ \hline Q-ROF & [87] \\ \hline Linguistic distribution assessment & [88] \\ \hline IVIF & [89] \\ \hline IVPF & [90] \\ \hline IT2F & [80] \\ \hline DHHLTS & [91] \\ \hline Spherical fuzzy sets & [92] \\ \hline TODIM & [88] \\ \hline C-MULTIMOORA & [93] \\ \hline EDAS & [94] \\ \hline ORESTE & [95] \\ \hline CoCoSo & [92] \\ \hline BWM & [92] \\ \hline AHP & [96] \\ \hline ANP & [97] \\ \hline DEMATEL & [92] \\ \hline \hline  & Artificial neural networks \\ \hline \begin{tabular}{c} Creating the intelligent system effect to address some of the weaknesses \\ inherent to the QFD approach (ambiguity in VOC, ill-defined strength of \\ relationship, complex and large HoQ, etc.). \\ \end{tabular} & \begin{tabular}{c} Fuzzy logic \\ Taguchi \\ \end{tabular} & [98] \\ \hline Enhanced classification of customer demand attributes; improved subjective & Fuzzy Kano & [99] \\ \hline Examining the nature of customer demands and enhancing their classification;  incorporating the results into the QFD analytic process & \begin{tabular}{c} Kano and nonlinear integer \\ programming model \\ \end{tabular} & [100] \\ \hline \begin{tabular}{c} Kano, DEMATEL, and nonlinear programming \\ \end{tabular} & [101] \\ \hline \begin{tabular}{c} Evaluating user expectations by using the probabilities of focal areas that are \\ of interest to the user, based on sustainability parameters, as hidden factors. \\ \end{tabular} & \begin{tabular}{c} Kano and MPAGA \\ \end{tabular} & [102] \\ \hline \begin{tabular}{c} Assessment of functional relationships under uncertainty. \\ \end{tabular} & \begin{tabular}{c} Asymmetric fuzzy linear regression \\ \end{tabular} & [104] \\ \hline The synthesis of different types of information generated during the QFD & Evidential Reasoning & [105] \\ \hline Reflecting on individuals’ attitudes toward risk; analyzing the correlation & IVIF sets, continuous ordered weighted \\ between customer demands; determining the relative weights of & \
\begin{tabular}{c} averaging aggregation operator, \\ K-additive measures, Choquet integral \\ and mixed-integer programming \\ \end{tabular} & [106] \\ \hline
\begin{tabular}{c} Analytic Hierarchy Process: AHP; Analytic Network Process: ANP; Best–Worst Method: BWM; Cloud Multi- \\ Objective Optimization by Ratio Analysis plus the full MULTIplicative form: C-MULTIMOORA; Combined COmpromise SOUtion: CoCoCo: Decision-Making Trial and Evaluation Laboratory: DEMATEL; Double-Hierarchy \\ Hesitant Linguistic Term Sets: DHHLTS; Evaluation based on Distance from Average Solution: EDAS; Hesitant Fuzzy Linguistic Term Set: HFLTS; Interval Type-2 Fuzzy: IT2F; interval-valued intuitionistic fuzzy: IVIF; interval-valued Pythagorean fuzzy: IVPF; Multi-Population Adaptive Genetic Algorithm: MPAGA; Organisation, rangement et Synthese de donnees relationnelles: ORESTE; q-ung orthopair fuzzy: Q-ROF; TOMada de Decisão \\ Interativa e Multicitericio: TODIM; voice of customer: VOC. \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 5: Overview of methodological improvements to the conventional QFD approach.