Sharifi, M., Yaghoubizadeh, M. (2015). Reliability Modelling of the Redundancy Allocation Problem in the Series-parallel Systems and Determining the System Optimal Parameters. Journal of Optimization in Industrial Engineering, 8(17), 67-77.

Mani Sharifi; Mohsen Yaghoubizadeh. "Reliability Modelling of the Redundancy Allocation Problem in the Series-parallel Systems and Determining the System Optimal Parameters". Journal of Optimization in Industrial Engineering, 8, 17, 2015, 67-77.

Sharifi, M., Yaghoubizadeh, M. (2015). 'Reliability Modelling of the Redundancy Allocation Problem in the Series-parallel Systems and Determining the System Optimal Parameters', Journal of Optimization in Industrial Engineering, 8(17), pp. 67-77.

Sharifi, M., Yaghoubizadeh, M. Reliability Modelling of the Redundancy Allocation Problem in the Series-parallel Systems and Determining the System Optimal Parameters. Journal of Optimization in Industrial Engineering, 2015; 8(17): 67-77.

Reliability Modelling of the Redundancy Allocation Problem in the Series-parallel Systems and Determining the System Optimal Parameters

^{1}Assistant Professor, Faculty of Industrial & Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

^{2}MSc, Faculty of Industrial & Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Abstract

Considering the increasingly high attention to quality, promoting the reliability of products during designing process has gained significant importance. In this study, we consider one of the current models of the reliability science and propose a non-linear programming model for redundancy allocation in the series-parallel systems according to the redundancy strategy and considering the assumption that the failure rate depends on the number of the active elements. The purpose of this model is to maximize the reliability of the system. Internal connection costs, which are the most common costs in electronic systems, are used in this model in order to reach the real-world conditions. To get the results from this model, we used meta-heuristic algorithms such as genetic algorithm and simulation annealing after optimizing their operators’ rates by using response surface methodology.

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