Redundancy allocation problem with a mixed strategy for a system with k-out-of-n subsystems and time-dependent failure rates based on Weibull distribution: An optimization via simulation approach

Guilani, Pedram Pourkarim; Azimi, Parham; Sharifi, Mani; Amiri, Maghsoud

doi:10.24200/sci.2018.20152

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…After that, many researchers considered the RAP for systems with different configurations. For example, Mishra and Rajamani 5 and Coit 6 used DP for solving the RAP with different configurations, while Pourkarim et al 7 used a joint Branch & Bound and optimization via simulation technique to solve a RAP with time-dependent failure rate components.…”

Section: General Rapmentioning

confidence: 99%

“…Selecting different switching devices for each subsystem will affect the system's total MTTF and cost. Hence, equation (1) will be developed into equation (7) in the bi-objective model. Also, in this model, the system's cost is considered a separate objective function (the system's cost was considered a constraint in the single-objective model using equation ( 2)).…”

Section: Optimization Modelsmentioning

confidence: 99%

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A Markov chain-based genetic algorithm for solving a redundancy allocation problem for a system with repairable warm standby components

Kayedpour

Amiri

Rafizadeh

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part O:

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Many studies have been conducted on designing systems based on the redundancy allocation problem (RAP). However, considering repairable warm-standby components (which are subject to failure even in an idle state) is somewhat neglected by researchers due to the complex mathematical models. One of the crucial aspects of these systems is considering the probability of failure when switching to a standby component or subsystem. This study tries to highlight these imperfect switching and switch selection strategies in the redundancy allocation designs. In this regard, this article is dedicated to developing two RAP models (a single objective and a bi-objective) with warm standby repairable components by proposing a solving approach based on the genetic algorithm (GA) and Markov chains. Since the model’s objective functions minimize the system’s mean time to failure (MTTF) and cost, we discussed how imperfect switches affect the total system’s cost and mean time to failure for the proposed RAPs. Finally, we adopted a GA and a non-dominated sorting genetic algorithm (NSGA-II) to solve the proposed models due to the models’ complexity. Solving these models clearly indicates the critical role of selecting an appropriate switching strategy on the system’s costs and reliability.

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Section: General Rapmentioning

confidence: 99%

Section: Optimization Modelsmentioning

confidence: 99%

A Markov chain-based genetic algorithm for solving a redundancy allocation problem for a system with repairable warm standby components

Kayedpour

Amiri

Rafizadeh

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part O:

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show abstract

“…Thus, in addition to the subsystem component count, it is possible to find the required redundancy strategy via the algorithm. 42–45 were able to implement RAP simulation techniques.…”

Section: Introductionmentioning

confidence: 99%

Redundancy allocation of heterogeneous components by simulation-based optimization

Chambari

Azimi

Najafi

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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The present study proposes a novel simulation-based optimization model for a series-parallel redundancy allocation problem (RAP) under heterogeneous components for reliability maximization subjected to system-level constraints through the identification of the optimal redundancy strategy, component type, and subsystem component count. To obtain higher practicality, active, cold-standby, mixed, and K-mixed redundancy strategies are incorporated as the decision variables. In general, as it is difficult to determine a closed-form (excluding active redundancy strategies), it is not possible to analytically assess system reliability. Earlier studies on system reliability optimization applied convenient lower bound approximation. This limits higher reliability levels. In order to tackle this problem, this study adopted simulation sampling to make unbiased efficient reliability estimates. To this end, 4Dscript interpreting programming was utilized to develop a simulation model. As RAP has a combinatorial nature and is a random and NP-hard problem, this study adopted the genetic algorithm (GA) optimization. To validate the model and assess GA efficiency, the numerical findings of some benchmark tests were employed. The proposed approach outperformed earlier approaches in reliability and confidence.

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Presenting a Series-Parallel Redundancy Allocation Problem with Multi-State Components Using Recursive Algorithm and Meta-Heuristic

Sharifi¹,

Saadvandi²,

Shahriari³

2018

Scientia Iranica

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Redundancy Allocation Problem (RAP) is one of the most important problems in the eld of reliability. This problem is aimed at increasing system reliability under constraints such as cost, weight, etc. This study works on a system with series-parallel con guration and multi-state components. To draw the problem nearer to the real condition, this study merges this problem with discount levels in purchasing components. For calculating the reliability of sub-systems, a recursive algorithm is used. Because the redundancy allocation problem belongs to NP-hard problems, for optimizing the presented model, a new Genetic Algorithm (GA) was used. The algorithm parameters were tuned using Response Surface Methodology (RSM), and an enumeration method was used for the validation of GA.

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Redundancy allocation problem with a mixed strategy for a system with k-out-of-n subsystems and time-dependent failure rates based on Weibull distribution: An optimization via simulation approach

Cited by 4 publications

References 27 publications

A Markov chain-based genetic algorithm for solving a redundancy allocation problem for a system with repairable warm standby components

A Markov chain-based genetic algorithm for solving a redundancy allocation problem for a system with repairable warm standby components

Redundancy allocation of heterogeneous components by simulation-based optimization

Presenting a Series-Parallel Redundancy Allocation Problem with Multi-State Components Using Recursive Algorithm and Meta-Heuristic

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