Reliability optimization of series-parallel systems with K-mixed redundancy strategy

Peiravi, Abdossaber; Karbasian, Mahdi; Ardakan, Mostafa Abouei; Coit, David W.

doi:10.1016/j.ress.2018.11.008

Cited by 65 publications

(22 citation statements)

References 23 publications

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“…Even though the true optimal allocation strategy is unknown, the allocation strategy found by GA after 1000 generations will be close to the optimal allocation as shown in the literature. 28,43,46,54,55 On the other hand, to avoid GA being trapped in local optimum, commonly used measures include increasing the rate of mutation and maintaining a diverse population of solutions. To further demonstrate that the solution found by GA are close to the true optimal solution, we implement GA again by increasing the mutation rate or update the population when the individuals are similar.…”

Section: Numerical Examplementioning

confidence: 99%

Optimal element allocation and sequencing of multi-state series systems with two levels of performance sharing

Xiao

Cao

Kou

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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Motivated by real-world complex engineering systems, this research proposes a multi-state system with two levels of performance sharing. In this system, there exists a unique performance transmitter between any two adjacent subsystems and any two adjacent elements within each subsystem. Surplus performance of a subsystem or an element can only be transmitted to its adjacent subsystems or elements. We build the reliability model of the proposed system and suggest a corresponding reliability evaluation algorithm by extending the existing universal generating function technique. Since the performance sharing is only allowed between adjacent subsystems and elements, the element allocation and sequencing will affect the system reliability. Due to the complexity of the combinatorial optimization problem, we use the genetic algorithm to find the optimal allocation and sequencing of the elements. An analytical example is provided to illustrate the reliability evaluation algorithm. Numerical experiments are carried out to demonstrate how the optimal allocation and sequencing as well as the capacities of the performance transmitters affect the system reliability.

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Section: Numerical Examplementioning

confidence: 99%

Optimal element allocation and sequencing of multi-state series systems with two levels of performance sharing

Xiao

Cao

Kou

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…14 Several mathematical RAP optimization techniques have been developed by various redundancy strategies, for example, active, standby, mixed, and k-mixed ones, for various purposes. 16,19–24 The present study proposes a novel simulation-based optimization model enabling the independent adoption of the aforementioned strategies toward subsystems.…”

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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“…However, the efficiency and accuracy of GA largely depend on its parent populations and stopping criterion, 20 especially for redundancy optimization with large number of available components. In the traditional GA, the number of designs in the parent population for each generation is usually randomly decided.…”

Section: Introductionmentioning

confidence: 99%

Redundancy optimization for multi-state series-parallel systems using ordinal optimization-based-genetic algorithm

Ding

Zeng

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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Multi-state series-parallel systems (MSSPSs) are widely-used for representing engineering systems. In real-life cases, engineers need to design an optimal MSSPS structure by combining different versions and number of redundant components. The objective of the design is to ensure reliability requirements using the least costs, which could be formulated as a redundancy optimization problem under reliability constraints. The genetic algorithm is one of the most frequently used method for solving redundancy optimization problems. In traditional genetic algorithms, the population size needs to be determined based on the experience of the modeler. Often, this ends up creating a large number of unnecessary samples. As a result, the computational burden can be huge, especially for large-scale MSSPS structures. To solve these problems, this paper proposes an optimal structure designing method named as redundancy ordinal optimization. The universal generating function technique is applied to evaluate the reliabilities of the MSSPSs. Based on the reliabilities, an ordinal optimization algorithm is adapted to update the parent populations and the stopping criterion of genetic algorithm, so that the unnecessary structure designs can be eliminated. Numerical examples show that the proposed method improves the computational efficiency while remaining satisfactorily accurate.

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Reliability optimization of series-parallel systems with K-mixed redundancy strategy

Cited by 65 publications

References 23 publications

Optimal element allocation and sequencing of multi-state series systems with two levels of performance sharing

Optimal element allocation and sequencing of multi-state series systems with two levels of performance sharing

Redundancy allocation of heterogeneous components by simulation-based optimization

Redundancy optimization for multi-state series-parallel systems using ordinal optimization-based-genetic algorithm

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