Reliability block diagram (RBD) is an effective tool for modeling and evaluating system reliability. During operation, a system’s reliability may decrease significantly due to the failure of certain critical nodes and thus should be reconfigured. This paper presents a framework for system reliability evaluation and dynamic optimization based on RBD, designed from the perspective of system users. First, we improve the classic RBD model with a new encoding scheme and develop an accurate RBD computation algorithm that is easily recognized by computers and highly efficient. Second, we create an optimization algorithm based on Tabu Search to reconfigure the system after node failure, striking a balance between system reliability recovery and RBD variation amplitude. Finally, we provide some numerical examples and a computational experiment based on a practical instance from a navy fleet to demonstrate the correctness and effectiveness of our proposed methods.
ManishMKishorST. Power-hierarchy of dependability-model types. IEEE Trans Reliab1994; 43(3): 493–502.
2.
CarnevaliLCianiLFantechiA, et al. An efficient library for reliability block diagram evaluation. Appl Sci2021; 11: 4046.
3.
RuijtersEStoelingaM. Fault tree analysis: a survey of the state-of-the-art in modeling, analysis and tools. Comput Sci Rev2015; 15–16(3): 29–62.
4.
ZhaoJCZhaoYXLuYX, et al. Overview of system reliability modeling tools. In: Proceedings of 7th international conference on education, management, computer and medicine, Shenyang, China, 29–31 December 2016. Amsterdam, the Netherlands: Atlantis Press.
5.
BrownJIColbournCJCoxD, et al. Network reliability: heading out on the highway. Networks2021; 77: 146–160.
6.
VasilyevAAndrewsJDunnettSJ, et al. Dynamic reliability assessment of PEM fuel cell systems. Reliab Eng Syst Saf2021; 210: 107539.
7.
MasettiGRobolLChiaradonnaS, et al. Implicit reward structures for implicit reliability models. IEEE Trans Reliab2023; 72(2): 774–793.
8.
CuibusOLetiaT. Approaching problems with different kinds of petri net models. In: 23rd IEEE international conference on automation, quality and testing, robotics, Cluj-Napoca, Romania, 19–21 May 2022. New York, NY: IEEE.
9.
XuHXingL.RobidouxR. Drbd: dynamic reliability block diagrams for system reliability modeling. Int J Comput Appl2009; 31(2): 132–141.
10.
ZhangJFDuX. Time-dependent reliability analysis for function generator mechanisms. J Mech Des2011; 133(3): 31005.
MoYXingLDuganBJ. MDD-based method for efficient analysis on phased-mission systems with multimode failures. IEEE Trans Syst Man Cybern Syst2014; 44(6): 757–769.
13.
BenabidRMerroucheDBourenaneA, et al. Reliability assessment of redundant electrical power supply systems using fault tree analysis, reliability block diagram, and Monte Carlo simulation methods. In: International conference on electrical sciences and technologies, Algiers, Algeria, 28–31 October 2018. New York, NY: IEEE.
14.
FerdinandoCAlessandraILucioC, et al. A general framework for dependability modelling coupling discrete-event and time-driven simulation. Reliab Eng Syst Saf2020; 199: 106904.
15.
RauzyA. New algorithms for fault trees analysis. Reliab Eng Syst Saf1993; 40(3): 203–211.
16.
ShresthaAXingLDDaiYS. Decision diagram based methods and complexity analysis for multi-state systems. IEEE Trans Reliab2010; 59(1): 145–161.
17.
JinGMatthewsD. Measurement plan optimization for degradation test design based on the bivariate wiener process. Qual Reliab Eng2015; 30(8): 1215–1231.
18.
LaxminarayanSAAsokeKBBParmadKKC. Genetic algorithm based multi-objective reliability optimization in interval environment. Comput Ind Eng2012; 62(1): 152–160.
19.
WangHGongZHuangHZ, et al. System reliability based design optimization with Monte Carlo simulation. In: International conference on quality, reliability, risk, maintenance, and safety engineering, Chengdu, China, 15–18 June 2012. New York, NY: IEEE.
20.
CatelaniMCianiLVenziM. RBD model-Based approach for reliability assessment in complex systems. IEEE Syst J2019; 13(3): 2089–2097.
21.
CiSZhangJCSharifH, et al. Dynamic reconfigurable multi-cell battery: a novel approach to improve battery performance. In: Twenty seventh annual IEEE applied power electronics conference and exposition, Orlando, FL, USA, 5–9 February 2012. New York, NY: IEEE.
22.
WuASPanRZhaoX, et al. Reliability evaluation of consecutive-k-out-of-n:f systems with two performance sharing groups. Comput Ind Eng2021; 153: 107092.
23.
GülpınarNÇanakogluEBrankeJ. Heuristics for the stochastic dynamic task-resource allocation problem with retry opportunities. Eur J Oper Res2018; 266: 291–303.
24.
LiuHBSongGPLiuTY, et al. Multitask emergency logistics planning under multimodal transportation. Mathematics2022; 10: 3624.
25.
Cheikh-GraietSBDotoliMHammadiS. A Tabu Search based metaheuristic for dynamic carpooling optimization. Comput Indu Eng2020; 140: 106217.1–106217.15.
26.
AlfieriACastiglioneCPastoreE. A multi-objective tabu search algorithm for product portfolio selection: a case study in the automotive industry. Comput Ind Eng2020; 142: 106382.
27.
LiXYGaoL. An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem. Int J Prod Econ2016; 174: 93–110.
