引用本文:李婧,陈光宇,唐菱,等.双层多态加权$k/n$系统可用性模型与冗余设计优化[J].控制与决策,2020,35(11):2752-2760
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双层多态加权$k/n$系统可用性模型与冗余设计优化
李婧1, 陈光宇1, 唐菱2, 王瑞琦1
(1. 电子科技大学经济与管理学院,成都611731;2. 中国工程物理研究院激光聚变研究中心,四川绵阳621900)
摘要:
鉴于当前多态系统可用性模型刻画分层性能要求的不足,定义一种更加通用的双层多态加权$k/n$系统,提出新的分层运算符结合通用生成函数的方法解决权重的跨层次依赖问题,建立系统可用度模型;针对多态系统冗余设计面临的“子系统和部件综合选择问题”,构建可用度约束下的系统总成本优化模型,利用遗传算法编程获取各子系统和部件的经济数量;以某供电系统为例验证所提出模型及方法的正确性和有效性,并对比说明双层性能要求对系统可用性和经济性的影响. 研究成果可为复杂系统在不同性能要求下的设计优化提供决策支持.
关键词:  多态系统  可用性  双层性能要求  通用生成函数  设计优化
DOI:10.13195/j.kzyjc.2018.1752
分类号:TP273
基金项目:国家自然科学基金项目(71531003).
Availability modeling and redundancy design optimization of dual hierarchical multi-state weighted bmk-out-of-bmn system
LI Jing1,CHEN Guang-yu1,TANG Ling2,WANG Rui-qi1
(1. School of Management and Economics,University of Electronic Science and Technology of China,Chengdu 611731,China;2. Research Center of Laser Fusion, China Academy of Engineering Physics,Mianyang 621900,China)
Abstract:
Aiming at the limitations of present multi-state systems in characterizing hierarchical performance requirements, a more general dual hierarchical multi-state weighted k-out-of-n system is defined. A new method combining hierarchical operators with universal generation functions is developed to solve the problem of weight dependencies across the different levels to access the availability of the proposed system. Then, the system total cost optimization model under the availability constraint is constructed to study the problem of integrated selection of subsystems and components in system design. The genetic algorithm is adopted to obtain the economic quantities of various subsystems and components. Finally, a power system is demonstrated for the correctness of the presented approach and the effects of the dual hierarchical performance requirements on system availability and cost. The results can offer implications for the design optimization of complex systems under different performance requirements.
Key words:  multi-state system  availability  dual hierarchical performance requirements  universal generating functions  design optimization

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