真實世界中，許多系統，如電力系統、電腦系統與運輸系統等，可藉由模擬成由傳輸邊及節點所組成的隨機流量網路(stochastic-flow network)，以分析該系統之績效。其中網路可靠度可定義為d單位的需求可以成功地藉由此網路自發送端傳送至接受端之機率。而從一個品質管理的觀點來看，網路可靠度最佳化(network reliability optimization)為對許多系統管理者所欲達成之目標。因此如何自現有之資源中，配置一組資源於網路之邊或節點上，使得網路可靠度最佳，為網路可靠度最佳化重要議題之一。
儘管有大量的文獻探討著網路可靠度最佳化的議題，然而大多數的研究並沒有考量到相關性失效(correlated failure)的因素之於網路可靠度最佳化的關係。在現實世界中，大規模的災害將會對網路造成相當範圍的損害並產生相關性失效。而此相關性失效將有可能對於網路可靠度造成影響。
本論文以隨機流量網路為網路模型並提出考量相關性失效因素的資源配置問題。同時，對於此問題我們發展出一個混合基因與禁忌搜尋演算法(hybrid GA-TS algorithm)，利用該演算法有效地找出具有最大網路可靠度值的最佳資源配置(resource assignment)。另外，我們採用了三個基準電腦網路進行實驗並針對數個柔性計算(soft computing algorithms)包括混合基因與禁忌搜尋演算法與粒子群最佳化演算法、蟻群最佳化演算法、基因演算法、禁忌搜尋演算法比較其計算效率。而從實驗結果發現在求解該考量相關性失效因素的資源配置問題下，本論文所提出的混合基因與禁忌搜尋演算法和其他柔性計算相比，可以擁有較好的品質和更有效的計算效率獲得最佳網路可靠度。

Many real-life systems such as electric power systems, computer systems, and transportation systems can be characterized as stochastic-flow networks (SFN) composed of both sets of arcs and nodes to analyzing its performance. Network reliability is defined as the probability that d units of demand are successfully transmitted from a source node to a sink node. From a perspective on quality management, network reliability optimization is the goal for most of system supervisors. Thus, how to determine the optimal resource assignment from a set of resources to maximize network the reliability is an important issue.
Despite the numerous researches on the topic of network reliability optimization, virtually most of the previous researches didn’t discuss network reliability optimization with the consideration of correlated failures. In the real world, a large-scale disaster may cause widespread damage to lead to correlated failures for the networks. And correlated failures may influence the network reliability.
This thesis is based on SFN model and proposes the resource assignment problem with correlated failures (RACF problem). And we develop a hybrid GA-TS algorithm (HGTA) to efficiently search the optimal resource assignment with the maximal network reliability. Three benchmark computer networks for the RACF problem are adopted to compare the computing efficiency of HGTA with those of other soft computing algorithms including PSO, ACO, GA, and TS. And the experimental results show the proposed HGTA has better quality and better computational efficiency than other algorithms for obtaining the maximal network reliability.

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