Browsing by Author "Chen, Shin-Guang"

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A permutation-and-backtrack approach for reliability evaluation in multistate information networks
(2020-05-15)
Chen, Shin-Guang
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Lin, Yi-Kuei
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工業工程與管理學系
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Department of Industrial Engineering and Management
Nowadays, people live heavily on information systems to handle their business and schedules. A reliable information system helps people smoothly and efficiently deal with their daily-life affairs. An information system usually consists of connected software and hardware components, and can be modeled by connected graphs. In network theory, such graphs are called multistate information networks. Therefore, the reliability of an information system can be evaluated by network theory, namely network reliability. The most popular approach to evaluate network reliability is the three-stage-approach (TSA), which involves (a) finding all minimal paths (MPs), (b) finding all d-system vectors (d-SVs), and (c) calculating the union probability of d-SVs for reliability. Approaches for creating all MPs has been reported. There is a need for a more efficient approach to generate all d-SVs for such calculation. In this article, an approach based on permutation-and-backtrack facility is proposed to generate all such d-SVs. The efficiency of this approach is generally accepted to be superior to the existing approaches. The proposed approach provides us an efficient, and simple way to speed up TSA as a more powerful tool for evaluating the reliability of multistate information networks. (C) 2020 Elsevier Inc. All rights reserved.
Reliability evaluation in terms of flow data mining for multistate networks
(1970-01-01)
Lin, Yi-Kuei
;
Chen, Shin-Guang
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
Network reliability is famous for its problem solving ability in several real-life applications. However, due to its NP-hard nature (Ball in IEEE Trans Reliab 35(3):230-238, 1986), researchers are devoted to the improvement of computational efficiency in various approaches. Although flow in networks depicts its combination properties, only few of them are useful in the calculation of network reliability. In some point of views, we call it mining in flow data. This paper presents techniques of how to efficiently do the flow data mining tasks. A skill based on backtrack and maximal flow is illustrated with examples and benchmarks. The results show that the proposed approach is valuable in the calculation of network reliability.