An efficient simulation optimization method for the redundancy allocation problem with a chance constraint

IF 2.7 4区 管理学 Q2 MANAGEMENT Journal of the Operational Research Society Pub Date : 2023-10-23 DOI:10.1080/01605682.2023.2272860
Kuo-Hao Chang, Chi-Ping Lin
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Abstract

AbstractWe explore the Redundancy Allocation Problem (RAP) under the objective of minimizing the cost of a production system of general topology in which system reliability is treated as a chance constraint. A novel simulation optimization-based solution method grounded in the concepts of the trust region and response surface methodology is proposed to efficiently solve the generalized RAP (GRAP) under random system survival times. The generalizability of the RAP model and efficiency of the solution method allows for our approach to be utilized in a wide variety of real-world applications. We demonstrate in a series of numerical experiments based on production systems of varying complexity that the finite convergence of the proposed method is much more efficient than the commonly-used genetic algorithm. It is shown that on a simple bridge network, only the proposed algorithm can find the true optimal solution to the GRAP under an allotted computational budget. On a complex network which includes series, parallel, and logical relationships, the proposed algorithm is also shown to find solutions to the GRAP which have substantially lower total system cost than those found by GA under a wide variety of scenarios.Keywords: Reliabilityredundancy allocation problemsimulation optimizationchance constraint AcknowledgementThis work was financially supported by National Science and Technology Council (Taiwan) and the “High Entropy Materials Center” from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis work was financially supported by National Science and Technology Council (Taiwan) and the “High Entropy Materials Center” from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.
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具有机会约束的冗余分配问题的一种高效仿真优化方法
摘要以一般拓扑下的生产系统成本最小为目标,将系统可靠性作为一个机会约束,研究了冗余分配问题。基于信任域和响应面方法的概念,提出了一种基于仿真优化的求解随机系统生存时间下广义RAP (GRAP)问题的新方法。RAP模型的通用性和解决方案方法的效率使得我们的方法可以在各种实际应用程序中使用。通过一系列基于不同复杂性生产系统的数值实验证明,该方法的有限收敛性比常用的遗传算法要有效得多。结果表明,在一个简单的桥梁网络中,只有本文提出的算法能够在给定的计算预算下找到GRAP的真正最优解。在包含串联、并行和逻辑关系的复杂网络上,所提出的算法也被证明可以在各种场景下找到比遗传算法所发现的总系统成本低得多的GRAP解。本工作由国家科学技术委员会(台湾)和“高熵材料中心”资助,该研究中心来自台湾教育部高等教育萌芽计划框架下的特色区域研究中心计划。披露声明作者未报告潜在的利益冲突。本研究由国家科学技术委员会(台湾)和台湾教育部高等教育萌芽计划框架下特色地区研究中心计划中的“高熵材料中心”提供资金支持。
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来源期刊
Journal of the Operational Research Society
Journal of the Operational Research Society 管理科学-运筹学与管理科学
CiteScore
6.80
自引率
13.90%
发文量
144
审稿时长
7.3 months
期刊介绍: JORS is an official journal of the Operational Research Society and publishes original research papers which cover the theory, practice, history or methodology of OR.
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