對於真實世界中的許多系統，可藉由模擬成網路型式以分析該系統之績效，如通信網路、物流網路、電腦網路與電力網路等，在該網路是邊(arc)及節點(node)所組成的。其中網路可靠度為許多系統重要績效指標之一。因此，可靠度敏感度分析是大多數系統管理者所欲達成之目標。近年來，如何建立有效方案來提升或穩固網路可靠度是個重要議題。例如，如何提高網路可靠度？如何減少網路可靠度降低？另外每一個邊容量視為隨機性，此網路可稱之隨機流量網路(stochastic-flow network)。
近年來的文獻大部分利用可靠度重要度量方法(importance measure)以探討影響網路可靠度的重要因素。在此，本研究提出三個敏感度分析技巧來探討邊的變化對網路可靠度的影響情形，包括機率的改變(changes in probability)、邊容量的改變(changes in arc capacity)以及改善的潛力(improvement potential)。然而，其主要目標是藉由這些分析結果找出提升網路可靠度的要點以及找出高度影響網路可靠度的邊。所有網路可靠度的計算方式結合最小路徑(Minimal Paths)與遞回不交和法(Recursive Sum of Disjoint Product)演算法。
本研究針對四個網路模型進行敏感度分析，分別為隨機流量網路、電力網路、電腦網路與專案網路。隨機流量網路是其它網路的基本模型，因此在隨機流量網路的敏感度分析將網路條件(傳輸邊容量、機率分配)設置相同。另外三個與日常生活息息相關的網路，藉由敏感度分析找出關鍵性的邊並提出有效的改善方法來提高或穩固網路可靠度。

For real world systems can be modeled a network for analyzing its performance, such as a logistics network, a computer network, an electric power network, in which the network is constructed by a set of arcs and a set of nodes. Therefore, sensitivity analysis of network reliability is the goal which most managers pursue. Recently, determine a plan for improving network reliability is a crucial issue. How to improve the network reliability and reduce decrease of network reliability? In addition, each arc/node owns several capacities with a probability distribution and may fail should be regarded as stochastic-flow networks.
In the past, most of researches extended importance measure to explore the important factor of network reliability. This study addresses three sensitivity analysis techniques to analyze the changes of arcs that will affect the network reliability, including changes in probability, changes in arc capacity and improvement potential. The main goal is to apply these solutions to obtain the characteristic of network and plan for improving network reliability. In addition, the network reliability is evaluated by Minimal Paths (MPs) and Recursive Sum of Disjoint Product (RSDP) algorithm.
Furthermore, sensitivity analysis techniques is applied to four network models, including stochastic-flow network, stochastic electric power network, time-based computer network and stochastic project network. The condition is set the same for sensitivity analysis in stochastic-flow network model. Other three network models related with our daily life, utilizing sensitivity analysis to figure out the way of improving network reliability or stably keeping network reliability.

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