Optimization of reliability redundancy allocation problem with component mixing and strategy selection for subsystems

Najmi, Ali; Ardakan, Mostafa Abouei; Javid, Youness

doi:10.1080/00949655.2021.1879080

Cited by 13 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yeh et al [54]Used a hybrid algorithm(SEB) based on simplified swarm optimization (SSO),Elite selection and boundry Search(BS) solving cold-standby RRAP. Najmi et al [38] used a parallel stochastic fractal search algorithm for mathematical model of RRAP with heterogeneous components. Ouyang et al [40] proposed an improved particle swarm optimization algorithm (PSO) for RRAP with CM and mixed redundancy strategy.…”

Section: Introductionmentioning

confidence: 99%

Optimization of multi-objective reliability redundancy allocation problem with non-homogeneous components using mixed redundancy strategy under uncertainty conditions

Noormohammadi

Najafi

Safari

et al. 2023

Preprint

View full text Add to dashboard Cite

In this paper, we introduce a new multi-objective model and solution method for the reliability-redundancy allocation problem (RRAP) in a series-parallel system to maximize system reliability and minimize total cost. Most studies on RRAP assume the components are homogeneous, the reliability of components is predefined, and redundancy strategies in each subsystem are considered cold-standby or active. Each of the above assumptions serves as a constraint that doesn’t broaden solution regions. In the proposed multi-objective model, the components are heterogeneous, and the reliability of components is uncertain. In addition, mixed strategies (cold-standby and active redundancies) can be used in each subsystem. The proposed optimization problem is an NP-hard problem and cannot be solved by exact algorithms. Therefore, it is necessary to use meta-heuristic algorithms to solve this problem. Since the proposed model is a multi-objective model, a multiple evolutionary algorithm called NSGA-II will be used to solve the problem. Lastly, the performance of the proposed mathematical model is assessed by a well-known problem-testing method. The optimization results show the effectiveness of the proposed model and prove that the proposed method outperforms the previous ones.

show abstract

Section: Introductionmentioning

confidence: 99%