## 3 Materials and Methods
### The Utilization of Fuzzy Logic in Multi-Criteria Decision-Making
Despite offering many advantages, the conventional QFD model faces criticisms due to limitations that hinder its effectiveness and potential applications. [PERSON] [74] highlights crucial optimization areas, addressing the main weaknesses of the conventional QFD concept, which consist of the following:  
* Advancements in the initial phases of the QFD process, focusing on creating new models to assess input data in the HoQ matrix;
* Improving QFD analysis efficiency by combining it with various quality engineering methods, such as competitive analysis and relation matrix creation;
* Eliminating common weaknesses inherent in the QFD process, including ambiguities and vagueness in requirement expressions, requirement conflicts, and similar issues. The conventional QFD analytical process faces several inherent limitations, primarily concerning expressing the experts' uncertain assessments, weighting request attributes, and prioritizing quality attributes, namely the following:  
* The importance rank of the attributes is determined by using a simple weighted averaging technique that can lead to biased ranking results;
* The relative importance of the request attributes heavily relies on the preference information provided by the customers; however, obtaining these weights may be difficult due to time pressure and the customers' limited knowledge and experience;
* The QFD analytical process models input variables through linguistic characterizations. Following the conventional concept, these variables are treated as precise and mapped onto a numerical scale with crisp values. However, the correlation assessments rely on subjective perceptions of representatives of personalized knowledge for a specific domain, which are based on their intuition, personal experiences, and insights, leading to uncertainties regarding subjective preferences, making it difficult to precisely articulate or analytically describe them. Moreover, there is hesitation in the assessment arising from a limited comprehension of the problem being considered, including other uncertainties related to human cognition, such as inconsistencies, and more. Given the considerations noted, the variables in the QFD analytical process ought to be treated as ambiguous. Despite the prevailing contemporary scientific discourse that highlights the shortcomings of its conventional model, QFD remains the preeminent methodology in its initial fields of application. Furthermore, the framework has experienced several methodological and conceptual modifications, reflecting the maturation of the approaches employed in its implementation. Specifically, QFD has evolved into a flexible methodological framework that can be adapted, expanded, or integrated with other methodologies and approaches to address the constraints of its conventional concept, improve the efficiency of the QFD analytic process, and, crucially, extend beyond conventional application domains. The literature outlines several methodological improvements to the conventional QFD approach, an exhaustive review of which is presented in the Table 5.  
Motivated by previous discussions, this paper introduces several QFD methodological enhancements. Specifically, it aims to construct a hybrid QFD model integrated with the IT2F set, AHP, and EDAS methods to overcome the shortcomings of the conventional QFD model. More specifically:  
1. The paper integrates uncertainty theory into the conventional QFD analytic process, allowing for handling ambiguous linguistic variables with approximate accuracy. In this context, the IT2 TrNs are employed to model the input data of the QFD analytic process. 2. The paper proposes an AHP-based request attribute prioritizing phase, aiming to facilitate a more systematic and objective evaluation of their relative significance. 3. The prioritization of quality attributes is approached as an MCDM issue, instead of relying on simplistic weighted average techniques. In this context, the proposed QFD process incorporates the IT2 FEDAS method yielding a more precise priority ranking.