A New Method for Solving Reliability Optimization Problem

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

doi:10.1109/tr.1980.5220700

Cited by 27 publications

(16 citation statements)

References 6 publications

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“…Component redundancies are updated using a redundancy allocation heuristic with fixed values of the component reliabilities. Then, a continuous NLP algorithm (Tillman, Hwang, and Kuo, 1977;Xu, Kuo, and Lin, 1990), or another iterative heuristic (Gopal, Aggarwal, and Gupta, 1980), solves the reliability allocation problem with the previously obtained redundancy allocation. This procedure is repeated until there is no more improvement in system reliability.…”

Section: Sensitivity Factor Design and Scaling Methodsmentioning

confidence: 99%

“…Although the proposed heuristic procedure significantly decreases the number of NLP to be solved by evaluating only at the terminals of the branches, we employ an approximation heuristic to decrease computation time further. The approximation heuristic is a modification of the reliability-redundancy allocation heuristic used by Gopal, Aggarwal, and Gupta (1980). Whenever a NLP solution is required, the approximation heuristic is performed to find an approximated solution.…”

Section: Nlp Methods and Approximation Heuristicmentioning

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%

“…Excluding the branch-and-bound method of Kuo et al (1987), all other heuristics (Gopal, Aggarwal, and Gupta, 1980;Tillman, Hwang, and Kuo, 1977;Xu, Kuo, and Lin, 1990) have a unique solution path, where the solution path is defined by a set of allocations from the initial solution to the final one. The single path iterative heuristics are generally very fast, but not robust.…”

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: Nlp Methods and Approximation Heuristicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…This proposed availability allocation problem extends the reliability-redundancy allocation problem ( [13] [6][10] [3] [14] [7] [4] [12][9] [16]), which includes two decision variables such as the number of component, the failure rate of each component in each subsystem, by adding one more decision variable, the repair rate of each component for each subsystem. For solving reliability-redundancy allocation problems, a couple of famous combination methods have been published.…”

Section: Introductionmentioning

confidence: 99%

Availability optimization for repairable parallel-series system by applying Tabu-GA combination method

Liu

2012

IEEE 10th International Conference on Industrial Informatics

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This study looks at the system availability optimization problem under different resource and design configuration constraints by applying Tabu-GA combination method. From the point of view of logistics engineering, availability optimization applied in the initial system development period, plays a key role to affect system reliability, system maintenance planning, logistics requirements, and related costs during system planned life cycle. Generally, system availability can be improved by increasing component reliability level, component repair rate or the number of components in each subsystem.This proposed model is to combine Tabu method with Genetic Algorithm to solve system availability optimization problem. Through our method applied in the initial phase of system design and development, we can find the optimal allocation of component redundancy number, reliability and maintainability levels to minimize the total system cost under different configuration constraints such as weight, volume, and system availability requirements.The proposed numerical examples are demonstrated based on different system constraint requirements and parameter values. Through a simulation study of 30 times of calculations by applying this proposed combination method, stable results are clearly showed. We also perform the sensitivity analysis based on the cost parameters associated with reliability level and maintenance rate to provide very helpful information for system design and development process. Finally, the performance of this proposed Tabu-GA combination method is compared with SA-GA combination method and it turns out to be very good results in many aspects.

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Reliability Optimization

Coit¹

2008

Encyclopedia of Quantitative Risk Analysis and Assessment

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Reliability optimization involves the simultaneous evaluation and selection of components and a system‐level design configuration that collectively meets all design constraints, and at the same time, optimizes some objective function, often system reliability or availability. Often these problems are very constrained because of physical or budgetary limitations. There are several variations of reliability optimization and there have been many attempts to determine optimal design configurations for different problem formulations. Applicable optimization methods include dynamic programming, integer programming, mixed integer and nonlinear programming, and metaheuristics, e.g., genetic algorithms (GA). Notable extensions to this problem include multistate reliability optimization and multiple‐objective system reliability optimization.

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A New Method for Solving Reliability Optimization Problem

Cited by 27 publications

References 6 publications

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

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

Availability optimization for repairable parallel-series system by applying Tabu-GA combination method

Reliability Optimization

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