4.7 Article

An Improved Merge Search Approach to Evaluate Reliability in Multistate Network Systems

期刊

IEEE TRANSACTIONS ON RELIABILITY
卷 71, 期 1, 页码 382-389

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TR.2021.3077980

关键词

Reliability; Mathematical model; Manganese; Computer network reliability; Planning; Complexity theory; Phase change materials; Exact enumeration; merge search; multistate network systems (MNSs); network reliability; permutation and backtrack

资金

  1. Ministry of Science and Technology, Taiwan, Republic of China [MOST 107-2221-E-236-004-MY3, MOST 105-2221-E-009188-MY3]

向作者/读者索取更多资源

The article proposes an improved merge search method for evaluating the reliability of network systems, and demonstrates its efficiency through benchmark comparisons with well-known algorithms. The results show that the proposed algorithm can save as much as 10 times in computation time compared to existing methods in the literature.
Many real-world problems, such as power line transmission planning, computer network management, water pipe planning, and traffic flow planning, can be modeled using multistate network systems. One of the most interesting aspects of these problems is that one typically wishes to know the reliability of such a plan, namely the network reliability. To evaluate the reliability of a specific network system, the three-stage method (TSM) is typically applied. TSM consists of 1) finding all minimal paths, 2) finding all $d$-system vectors, and 3) calculating the union probability for reliability, where 1) is one of the most challenging problems discussed in the literature. This article proposes an improved merge search method that combines the advantages of both enumeration and searching technologies. A step-by-step exploration of the proposed method is presented and several benchmark comparisons are performed using well-known algorithms to demonstrate the efficiency of the proposed method. The results show that the computation time of the proposed algorithm can save as much as 10 times against the well-known methods in the literature.

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