A robust loss function approach for a multi-objective redundancy allocation problem

Salmasnia, Ali; Ameri, Ehsan; Niaki, Seyed Taghi Akhavan

doi:10.1016/j.apm.2015.06.007

Cited by 13 publications

(4 citation statements)

References 39 publications

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“…Artificial immune system algorithms, [9] improved surrogate constraint methods [10] and Tabu search [16] have been successfully implemented as well. [21] have taken into account, the variability data of reliability of components, gathered through field tests. [22] have used an electromagnetism like mechanism to solve the redundancy allocation problem.…”

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confidence: 99%

Design and analysis of the redundancy allocation problem using a greedy technique

Nanda¹,

Sharma²,

Kundnani³

et al. 2016

Preprint

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We present a very computationally light and fast approximation algorithm and then verify it with genetic algorithm and simulated annealing. We show that our algorithm is on par with GA and SA in terms of output produced while having a tightly bounded time complexity. Our algorithm works best when there is a strong positive correlation between the reliability of a component and its cost. We present two algorithms with the same essence. One of them is system cost bounded and the other is target reliability bounded. Our proposed algorithm works on a subsystem level redundancy instead of component level redundancy.

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Section: = ∑mentioning

confidence: 99%

Design and analysis of the redundancy allocation problem using a greedy technique

Nanda¹,

Sharma²,

Kundnani³

et al. 2016

Preprint

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“…Safari [43] considered system reliability and cost as objective functions and solved the model presented by Coit [14] using the NSGA-II algorithm. Salmasnia et al [45] considered RAP with multi-objective functions as maximizing the system reliability estimate, minimizing the overall system cost, minimizing system reliability variance estimate, and minimizing overall system cost variance. Taboada and Coit [47] considered RAP in the form of a multi-objective problem with three objective functions: maximizing system reliability, minimizing system cost, and weight.…”

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

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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.

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“…By integrating the objective functions in one function and application of physical programming and genetic algorithm, redundancy allocation problem was optimized (Wang and Kuo 2000). Through a method based on loss and by objective function variance reduction approach, multi-objective redundancy allocation problem was optimized in Salmasnia et al (2016). By consideration of redundancy allocation in phase manner, this problem was optimized in Mousavi et al (2015).…”

Section: Introductionmentioning

confidence: 99%

A redundancy allocation problem by using utility function method and ant colony optimization: tradeoff between availability and total cost

Salmasnia

Noori

Mokhtari

2019

Int J Syst Assur Eng Manag

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Redundancy allocation problem includes selection of several redundant components for allocating to a system; to maximize the availability, by considering the system constrains. In this regard, traditional prioritization methods have been presented which mainly addressed single-objective optimization. Moreover, the presented methods, due to have high number of solutions, are practically limited and selection of the best solution among the efficient solutions is typically complicated. In this study, the availability and the cost of system are regarded as objective functions. The corrective maintenance and allunit discount for buying the substituted components are considered in the model. To optimize the presented model, a solution prioritizing method with utility function and ant colony optimization will be presented. The method is a robust one in the case of existence of a relationship between availability and system cost. Finally, the corrective maintenance and all-unit discount of the proposed method will be investigated and the results will be examined in a numerical example.

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A robust loss function approach for a multi-objective redundancy allocation problem

Cited by 13 publications

References 39 publications

Design and analysis of the redundancy allocation problem using a greedy technique

Design and analysis of the redundancy allocation problem using a greedy technique

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

A redundancy allocation problem by using utility function method and ant colony optimization: tradeoff between availability and total cost

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