品質成本可視為衡量品質好壞的一種方法。許多企業運用品質成本資料從事分析與決策，然而品質成本項目之間的關係性相當複雜，每個企業的品質成本習性亦不同，且隨著時間演變，這些關係性也會變動，而導致品質成本資料具有不精確性、模糊性。因此，本研究以模糊理論為基礎，提出品質成本改善問題的方案評估法和模糊目標規劃法。在品質成本衡量系統中，結合隱藏成本，本研究提出一個模糊評估方法，利用證據整合技術--Choquet模糊積分法，整合有關的品質成本資訊，最後，提出一個綜合指標，來決定最佳的品質改善方案。另一方面，礙於品質成本項目的關係複雜，欲建構單一品質成本關係式並不容易，且管理者期望達成的品質成本目標可能互相衝突，而考慮目標的達成有一優先順序，因此，我們提出一個模糊目標規劃模式來處理此一決策問題。
為了提升顧客滿意度，品質機能展開(QFD)是一個有效的產品開發工具。在有限資源及為了提升市場的競爭性，必須決定工程設計需求，但礙於QFD規劃過程中，有許多專家的主觀評估，這些不精確的資訊不適用傳統的量化方法。因此，我們提出一個模糊線性規劃模式和模糊目標規劃模式，來協助設計人員決定最適的工程設計執行度。此方法不僅考慮顧客需求與工程設計需求的模糊關係，也考慮工程設計需求之間的模糊關係，並提出一個處理模糊數的新方法，來降低關係強度的模糊性。

Quality cost is usually considered as a means to measure the quality level in a quality system. In the assessments of quality cost, some hidden quality costs, such as the goodwill loss due to lost customers' reliability, are often neglected in the existing analysis methods. This may lead to reach a sub-optimal decision. In addition, the assessments of quantitative quality cost items are usually approximated, and therefore are imprecise in nature. Based on these considerations, we propose fuzzy approaches to evaluate quality improvement alternatives. An evidence fusion technique, namely Choquet fuzzy integral, is employed to aggregate the quality cost information. A composite index is determined to find the best quality improvement alternative. In general, a quality manager has to determine effective ways to perform quality cost improvement problems. However, the interrelationship among quality cost components is usually complex, such that it is inappropriate to construct a single quantitative relationship model in improving the quality. The improvements for different quality cost components may be conflict and may have preemptive priorities. Based on these considerations, we apply fuzzy goal programming models to formulate each quantitative quality cost relationship as a goal. Under different possibility levels, the proposed approach can attain the maximal sum of achievement degrees of all fuzzy quality cost improvement goals.
Quality function deployment (QFD) is the product development process to maximize customer satisfaction. The engineering design characteristics related to product performance are specified for the purpose. For dealing with the fuzzy nature in the product design processes, fuzzy approaches are applied to represent the relationships between customer requirements (CRs) and engineering design requirements (DRs) and among DRs. A new measure for evaluating the fuzzy normalized relationships is derived. A fuzzy model is formulated to determine the fulfillment level of each DR for maximizing the customer satisfaction under the resource limitation and the considerations of technical difficulty, and market competition. From the viewpoints of designers usually more than one goal is taken into account. In addition to customer satisfaction, the cost and technical difficulty of DRs are also considered as the other two goals, and are evaluated by linguistic terms. A fuzzy goal programming model is proposed to determine the fulfillment levels of the DRs. Different from the existing fuzzy goal programming models, the coefficients in the proposed model are also fuzzy in order to expose the fuzziness of the linguistic information. The models also consider the preemptive priorities between the goals. Under different possibility levels, the proposed approach can attain the maximal sum of achievement degrees of all goals.

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