品質機能展開(Quality function deployment, QFD)是目前企業常用於分析顧客需求與設計需求的一套系統化方法。而在品質機能展開的品質屋建構過程中有許多的數值與品質表必須被完成，包括：顧客需求的重要性、關係矩陣與相關矩陣等。然而，在以往的文獻中這些數值或表往往都假設為已知，但在實務上這些數值大多都是由一組專家，或者不同部門間所組成的工作團隊，經由一些決策流程，有效的將這些專家間或工作團隊成員間的意見整合出一共識值後所完成的，故其應屬於一群體決策(group decision-making, GDM)之問題。此外，專家常常以不同形式之明確值或模糊數等評比方式(例如：效用函數或語意變數等)表示其意見，因此考慮品質機能展開中品質屋建立之群體決策特性，本研究將分成兩個部份。第一部分中將以模糊群聚分析中常用的模糊C均值(Fuzzy C-Means, FCM)演算法為基礎，以α截集與群體決策的概念導入該演算法後，提出一個修正FCM演算法，以處理明確值或模糊數之專家意見。第二部份中，將該演算法導入QFD的品質屋建構流程中，將品質屋中專家間的意見進行分群，讓同一集群中的專家意見有比較高的相似性，接著再給予每個集群不同的權重後，將專家間的意見整合起來，成為一般文獻中所得到的專家意見共識值，以供後續研究之使用。最後，除了以上的群體決策方法，本研究也將導入關係矩陣正規化方法與模糊數排序方法，以找出設計需求重要性之排序，並進一步將以上概念與品質機能展開原始的基本流程，整合成一品質機能展開的群體決策流程。

Quality Function Deployment (QFD) is a well known method for enterprises to analyze the relationship between customer requirements (CRs) and design requirements (DRs), and there has been much research into it. In the processes of constructing the house of quality (HOQ) of QFD, there are several values and quality matrices that must be completed by experts, including the importance of customer requirements, relationship matrix and correlation matrix. In most of the research, the values and matrices are assumed to be known by authors or they only show the result of them. In practice, a group of experts or a team will fill in the values and matrices with crisp or fuzzy numbers, such as utility or linguistic variables; therefore, the process of filling in the values and matrices should be considered as a group decision-making (GDM) problem, and there may be some similarity between different experts＇opinions. In the first section of this study, we will introduce a new clustering algorithm, which we modify the well known clustering algorithm, in which we modify the well known clustering algorithm, Fuzzy C-Means (FCM), in order to cluster experts’crisp or fuzzy opinions and make those in the same cluster have higher similarity. After this, we will aggregate the clusters into a consensus for further use. Second, we will use the new method to cluster and aggregate the experts’ opinions on the CR importance, relationship matrix and correlation matrix in order to construct the HOQ of QFD. Finally, we will use the relationship normalizing method and fuzzy number ranking method to find out the ranking of the importance of DRs.

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