網路可靠度 (Network reliability) 是以巨觀的方式進行系統績效的評估，能同時提供客觀的系統管理與網路績效改善的特色，進而得到有助於瞭解系統執行品質與能力的寶貴資訊，以提供既有系統之維護或改善，及新建系統之設計、運作或規劃時重要的參考依據。因此近幾年來，網路可靠度被學者們廣泛的應用於各種不同的問題和案例中 (如：生產製造系統、電力網路、電腦運算及供應鏈配送等)，其中又以隨機供應鏈問題 (Stochastic Supply Chain Network; SSCN)之研究最為繁盛。在一般網路圖中，節點 (node) 代表供應鏈中的供應商、顧客和轉運站；弧 (arc) 則代表供應鏈中的可供運送商品的路徑，同時供應鏈在各路徑上的運送量可能因氣候、人為等外在因素導致其運送量不一致，因此形成隨機流量網路 (Stochastic Flow Network) 。
本研究主要採用網路可靠度來解決SSCN的問題，並充分考量多面相且具創新之供應鏈因子 (如：運輸時間限制、商品衰退、多商品運送及總運輸成本)，此外本研究所使用的網路可靠度演算法為直接演算法，與過去的間接演算法相比，其時間複雜度與運算時間接較為快速與直觀。

Network reliability is a magnificent way to evaluate the performance of the system, while providing objective system management and the improvement features of network, it also provides valuable information that will help us understand the quality and ability of the system and also tells us the important reference about the design, operation, or planning of new systems. So in recent years, network reliability has been widely used by scholars in a variety of different problems and cases (such as: manufacturing systems, power networks, computer computing and supply chain distribution, etc.), but the most prosperous research is Stochastic Supply Chain Network (SSCN) problem. The reason why people pay attention to this research is that network reliability can be the most appropriate application to the problem of SSCN. In network model, vertex denotes a supplier, a transfer center, or customer, while a route connecting a pair of vertices denotes a carrier. Each carrier will have their available transportation capacity and the capacity is not deterministic due to transportation capacity may be shared by different customer. Thus, we can view it as a SSCN.
In this research, network reliability is used to solve the problem of SSCN and also consider multi-faceted and innovative factors of supply chain (such as transportation time limit, commodity deterioration, multi-commodity transportation and total transportation cost). The network reliability algorithm used in this research is direct algorithm which was proposed by professor Wei-Chang Yeh, its’ time complexity and computation time are faster and more intuitive than the past indirect algorithm.

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