On optimal allocation of redundancies in random weighted k $$ k $$ -out-of- n $$ n $$ systems

IF 1.3 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Applied Stochastic Models in Business and Industry Pub Date : 2024-05-29 DOI:10.1002/asmb.2875
Tanmay Sahoo, Nil Kamal Hazra
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Abstract

Random weighted k $$ k $$ -out-of- n $$ n $$ systems are very useful in modeling the lifetimes of systems, wherein the success or failure of a system depends not only on its current operational status, but also on the contributions made by its components. In this paper, we consider random weighted k $$ k $$ -out-of- n $$ n $$ systems with redundant components drawn randomly from a mixed population consisting of m $$ m $$ different subpopulations/substocks. We study different optimal allocation policies of active redundancies and minimal repair components in a random weighted k $$ k $$ -out-of- n $$ n $$ system. Moreover, we investigate how the heterogeneity of subpopulations of items impacts the lifetime of a random weighted k $$ k $$ -out-of- n $$ n $$ system. We also present some simulational results and a real data analysis for illustrative purpose.

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论随机加权 k$$ k $$-out-of-n$ n $$ 系统中冗余的最优分配
随机加权损耗系统对系统寿命建模非常有用,因为系统的成败不仅取决于其当前的运行状况,还取决于其组件的贡献。在本文中,我们考虑的是随机加权 "缺失 "系统,其冗余组件是从由不同亚群/子群组成的混合群中随机抽取的。我们研究了随机加权出错系统中主动冗余和最小修复组件的不同最优分配策略。此外,我们还研究了项目子群的异质性如何影响随机加权出错系统的寿命。我们还介绍了一些模拟结果和真实数据分析,以作说明。
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来源期刊
CiteScore
2.70
自引率
0.00%
发文量
67
审稿时长
>12 weeks
期刊介绍: ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.
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