An Improved Algorithm for Reliability Optimization

Gopal, Krishna; Aggarwal, K. K.; Gupta, Jaya

doi:10.1109/tr.1978.5220403

Cited by 63 publications

(20 citation statements)

References 16 publications

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“…The main procedure of most iterative heuristics for reliability optimization problems is based on redundancy allocation heuristics (Gopal, Aggarwal, and Gupta, 1978) with sensitivity factors. Component redundancies are updated using a redundancy allocation heuristic with fixed values of the component reliabilities.…”

Section: Sensitivity Factor Design and Scaling Methodsmentioning

confidence: 99%

“…From a given initial solution, they used a greedy heuristic to solve the redundancy allocation problem, and the Hooke-Jeeves method to find the optimal reliability allocation with the obtained redundancy allocation solution, alternatively, until the solution satisfied the predefined criteria. Gopal, Aggarwal, and Gupta (1980) applied the same heuristic which they developed for the redundancy allocation problems (Gopal, Aggarwal, and Gupta, 1978). Their method iteratively increases the redundancy by 1 or the component reliability by a predefined amount, which is computed using sensitivity factors.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Path Approach for Reliability-Redundancy Allocation Using a Scaling Method

Kuo

2005

J Heuristics

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The reliability-redundancy allocation problem is an optimization problem that achieves better system reliability by determining levels of component redundancies and reliabilities simultaneously. The problem is classified with the hardest problems in the reliability optimization field because the decision variables are mixed-integer and the system reliability function is nonlinear, non-separable, and non-convex. Thus, iterative heuristics are highly recommended for solving the problem due to their reasonable solution quality and relatively short computation time. At present, most iterative heuristics use sensitivity factors to select an appropriate variable which significantly improves the system reliability. The sensitivity factor represents the impact amount of each variable to the system reliability at a designated iteration. However, these heuristics are inefficient in terms of solution quality and computation time because the sensitivity factor calculations are performed only at integer variables. It results in degradation of the exploration and growth in the number of subsequent continuous nonlinear programming (NLP) subproblems. To overcome the drawbacks of existing iterative heuristics, we propose a new scaling method based on the multipath iterative heuristics introduced by Ha (2004). The scaling method is able to compute sensitivity factors for all decision variables and results in a decreased number of NLP subproblems. In addition, the approximation heuristic for NLP subproblems helps to avoid redundant computation of NLP subproblems caused by outlined solution candidates. Numerical experimental results show that the proposed heuristic is superior to the best existing heuristic in terms of solution quality and computation time.

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Section: Sensitivity Factor Design and Scaling Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Path Approach for Reliability-Redundancy Allocation Using a Scaling Method

Kuo

2005

J Heuristics

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“…Complex (bridge) system [13] [14] is described as Figure. The formula is: 5 4 3 2 1 5 4 3 2 5 4 3 1 5 4 2 1 5 3 2 1 4 3 2 1 5 3 2 5 4 1 4 3 2 …”

Section: P2: Complex (Bridge) Systemmentioning

confidence: 99%

A Mixed TS-ISA Algorithm for Reliability Redundancy Optimization Problem

Zhu¹,

Liu²

2016

IJSIA

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“…An exact method based on the branch-and-bound procedure and a Tabu search-based algorithm are proposed in Lust, Roux, and Riane (2009). Khatab, Ait-Kadi, and Nourelfath (2007) proposed two heuristic methods, adapted from those used to solve the redundancy allocation problem (Aggrawal 1976;Gopal, Aggrawal, and Gupta 1978;Sharma and Venkateswaran 1971). Maaroufi, Chelbi, and Rezg (2013) studied the selective maintenance problem for a system where some components are subject to both global failure propagation and isolation.…”

Section: Introductionmentioning

confidence: 99%

Selective maintenance optimisation for series-parallel systems alternating missions and scheduled breaks with stochastic durations

Khatab¹,

Aghezzaf

Diallo

et al. 2017

International Journal of Production Research

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This paper deals with the selective maintenance problem for a multi-component system performing consecutive missions separated by scheduled breaks. To increase the probability of successfully completing its next mission, the system components are maintained during the break. A list of potential imperfect maintenance actions on each component, ranging from minimal repair to replacement is available. The general hybrid hazard rate approach is used to model the reliability improvement of the system components. Durations of the maintenance actions, the mission and the breaks are stochastic with known probability distributions. The resulting optimisation problem is modelled as a non-linear stochastic programme. Its objective is to determine a cost-optimal subset of maintenance actions to be performed on the components given the limited stochastic duration of the break and the minimum system reliability level required to complete the next mission. The fundamental concepts and relevant parameters of this decision-making problem are developed and discussed. Numerical experiments are provided to demonstrate the added value of solving this selective maintenance problem as a stochastic optimisation programme.

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An Improved Algorithm for Reliability Optimization

Cited by 63 publications

References 16 publications

Multi-Path Approach for Reliability-Redundancy Allocation Using a Scaling Method

Multi-Path Approach for Reliability-Redundancy Allocation Using a Scaling Method

A Mixed TS-ISA Algorithm for Reliability Redundancy Optimization Problem

Selective maintenance optimisation for series-parallel systems alternating missions and scheduled breaks with stochastic durations

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