###### Abstract  
As a powerful tool for improving customer satisfaction, quality function deployment (QFD) can convert customer requirements (CRs) into engineering characteristics (ECs) during product development and design. Aiming to address the deficiencies of traditional QFD in expert evaluation, CRs' weight determination and ECs' importance ranking, this paper proposes an enhanced QFD model that integrates hesitant fuzzy binary semantic variables, the Best-Worst Method (BWM), and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The objective is to determine the prioritization of product engineering characteristics. Indeed, hesitant fuzzy linguistic term sets (HFLTS) have found extensive application in decision-making problems. Compared to other fuzzy language methods, HFLTS offers greater convenience and flexibility in addressing decision-makers' hesitations and uncertainties. Initially, the combination of hesitant fuzzy linguistic term sets with interval binary tuple language variables is employed to articulate the uncertainty in the assessment information provided by QFD team members. Subsequently, the improved BWM and TOPSIS methods based on HFLTS are used to improve the accuracy of the importance ranking of engineering characteristics by determining the weights of CRs and prioritizing ECs in two stages. Finally, the feasibility and effectiveness of the proposed method are validated through an illustrative example. quality function deployment; hesitant fuzzy linguistic term set; 2-tuple linguistic model; Best-Worst Method; technique for order preference by similarity to ideal solution +
Footnote †: journal: _Applied_  
been conducted on QFD theory and application in academia, which has greatly expanded the research scope, perspective, and application fields of QFD. However, with the changing market environment and increasingly heterogeneous customer demands, product development faces various uncertain and fuzzy evaluation information, and the adaptability of traditional QFD methods is limited. This is primarily evident through the following aspects: (1) Traditional QFD requires experts to assess the correlation between CRs and ECs using score coefficients such as 1-3-9 or 1-5-9, but the subjectivity and fuzziness inherent in experts themselves make the evaluation results lack accuracy and reliability [12; 13]; (2) The traditional QFD method determines the importance of customer requirements based on customer evaluations, without conducting structured pairwise comparisons of customer requirements [12; 14]; and (3) Traditional QFD employs a basic approach of weighted arithmetic mean computation for calculating the significance of ECs, which fails to fully consider the decision-makers preference behaviors [15]. To address these issues, the academic community has conducted a large amount of research on improving the QFD method. On one hand, due to various mutual constraints and dependencies between CRs and ECs, there exist issues such as information fuzziness and data redundancy in the different parts of the House of Quality (HOQ). In addressing problems related to uncertain and imprecise information, [PERSON] introduced the theory of hesitant fuzzy sets (HFSs), which has rapidly gained application and development. As an extension of HFSs, [PERSON] et al. [16] proposed HFLTSs to handle situations in which decision-makers hesitate between several possible linguistic values or when considering richer expressions than a single linguistic term in the assessment of indicators, alternative variables, etc. In comparison to other fuzzy language methods, HFLTSs offer greater convenience and flexibility when managing decision-makers' hesitation and uncertainty in practical applications, and are more consistent with actual evaluation information when expressing evaluation information [17].