近年來，隨者產品製造技術提升，產品所面對的製造與使用環境趨向複雜化，在消費者意識高漲的情況下，賣方紛紛將產品保固當成一項商品的形式販賣。然而傳統保固期的估算，多以布林邏輯（Boolean）與機率演算（Probability Computation）加以評估可靠度工程，但是真實的世界中存在著許多似是而非的現象，也就是說它不再是傳統的集合理論中非此即彼的觀念，即嚴重失效與可維修之間存在著一個中間過渡狀態，這種過渡的連續性造成劃分的不確定性，Zadeh(1965)提出模糊的觀念也就應需而生。

　　本研究考量廠商所制定保固契約中，產品失效後的維修策略為小修理與置換策略，然而廠商對未知的小修理與置換策略，若無法擁有完整的經驗與維修資訊，零件的可靠度將充滿著模糊（fuzziness）不確定，並非只具有隨機性，對於可靠度機率模型的評估上僅能決定失效率，卻不足以反應不確定性下的小修理與置換行為。

　　此時傳統的明確集合模糊化，利用 α-cut的概念，推廣至模糊集合，使決策者更可針對產品失效狀態與經驗、資訊收集是否明確，來取得保固成本，再透過產品保固價格估算與保固成本之差，依據模糊推演，求得產品保固成本與廠商維修經驗與資訊是否充分有著反向之關係，廠商若有充份的維修經驗與資訊，將符合傳統可靠度之模式，保固成本也會降低。

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