Solving the Redundancy Allocation Problem With a Mix of Components Using the Improved Surrogate Constraint Method

Solving the Redundancy Allocation Problem With a Mix of Components Using the Improved Surrogate Constraint Method
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DOI:
10.1109/tr.2006.884602
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发表时间:
2007-03
影响因子:
5.9
通讯作者:
Junichi Onishi;Sakuo Kimura;R. James;Yuji Nakagawa
Junichi Onishi;Sakuo Kimura;R. James;Yuji Nakagawa
中科院分区:
计算机科学2区
文献类型:
--
作者:
Junichi Onishi;Sakuo Kimura;R. James;Yuji Nakagawa

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在设计系统时,有两种方法可用于在不改变系统性质的情况下提高系统的可靠性:1)使用更可靠的组件,和/或2)在系统内提供冗余组件。冗余分配问题试图在系统中找到适当的组件和冗余组合,以便在最低可靠性水平下最小化成本,或在最大成本和重量下最大化可靠性。冗余分配问题可以分为两类;一类允许系统中包含具有不同特性的组件,而另一类只允许每个组件有一种类型。前一组具有更大的解决方案空间相比,后者,因此,获得一个精确的最佳甚至高质量的解决方案,这个问题可能是更困难的。基于元启发式方法的优化技术最近被提出来解决具有混合组件的冗余分配问题。然而,一个精确的解决方法尚未开发。在本文中,我们发展了一种精确解方法,基于改进的替代约束(ISC)方法,并使用这种方法来寻找以前文献中提出的问题的最优解
When designing a system, there are two methods that can be used to improve the system's reliability without changing the nature of the system: 1) using more reliable components, and/or 2) providing redundant components within the system. The redundancy allocation problem attempts to find the appropriate mix of components & redundancies within a system in order to either minimize cost subject to a minimum level of reliability, or maximize reliability subject to a maximum cost and weight. Redundancy allocation problems can be classified into two groups; one allows the system to have a mix of components with different characteristics incorporated in the system, while the other only allows one type of each component. The former group has a much larger solution space compared to the latter, and therefore obtaining an exact optimal or even a high quality solution for this problem may be more difficult. Optimization techniques, based on meta-heuristic approaches, have recently been proposed to solve the redundancy allocation problem with a mix of components. However, an exact solution method has not been developed. In this paper, we develop an exact solution method, based on the improved surrogate constraint (ISC) method, and use this method to find optimal solutions to problems previously presented in the literature