Redundancy allocation problem in a bridge system with dependent subsystems

Abstract

The redundancy allocation problem is an important problem in system reliability design. Many researchers have investigated the redundancy allocation problem under different assumptions and for various system configurations. However, most of the studies have disregarded the dependence among components and subsystems. In real-world applications, the performance of components and subsystems can affect each others. For instance, the heat radiated by a subsystem can accelerate degradation of adjacent components or subsystems. In this article, a procedure is proposed for solving the redundancy allocation problem of a bridge structure with dependent subsystems. Copula theory is utilized for modeling dependence among subsystems, and artificial neural network and particle swarm optimization are applied for finding the best redundancy allocation. A numerical example is included to elaborate the proposed procedure and show its applicability.

Redundancy mechanisms to systems with statistically dependent component and redundancy lifetimes

“…Subsequently, Fang and Li (2016) had a discussion on the allocation of one active redundancy to coherent systems with dependent and heterogeneous component lifetimes, and Fang and Li (2017) explored the matched active redundancy allocation for coherent systems with statistically dependent component lifetimes. Recently, Sabri‐Laghaie et al (2019) investigated the redundancy allocation problem of a bridge system with dependent subsystems, and You et al (2023) took the first step to incorporate the statistical dependence into component and redundancy lifetimes and examined the stochastic comparison on the corresponding redundant system lifetimes. Among others, researchers utilized either copula or stochastic arrangement increasing distributions to model the dependence among lifetimes involved in the context of study.…”

Reliability-redundancy allocation Problem of a queueing system considering energy consumption

“…Failure in queueing systems can impose severe impacts on customer satisfaction and future profitability of industries. Therefore, much more attention should be given to the reliability of queueing systems [5]. In this regard, RRAP can be useful in the design process of these systems.…”

“…Due to the complexity of the RRAP, most of the researches has focused on developing heuristic and metaheuristic approaches. In this regard, we can refer to Simulated Annealing (SA) [8], Genetic Algorithm (GA) [4,[9][10][11][12], Particle Swarm Optimization (PSO) [5,6,[13][14][15][16], Artificial bee colony algorithm [3,17], Artificial immune search [18], Biogeography-based optimization (BBO) [19], fruit fly optimization algorithm [20], Markov decision process [21], Stochastic Fractal Search (SFS) [22], and hybrid algorithms such as SFS-GA [23]. In addition to heuristic and metaheuristic algorithms, simulation-based solution approaches [24] and exact solution methods such as implicit enumeration, branch-and-bound, and dynamic programming have also been used to solve RRAP [23].…”

ML-based group method of data handling: an improvement on the conventional GMDH

“…Following the aforementioned works, this study aims to enhance the ability of GMDH to handle more complex relationships between inputs and outputs, which has not been considered before. Considering the reasonable results of ML models in different regression and pattern recognition applications [20][21][22][23][24][25][26][27][28][29][30], it is valuable for us to study whether the combination of ML models and GMDH leads to better performance. A modified version of the GMDH is proposed, in which the basic polynomial functions are replaced by ML models.…”