Sharifi, M., Mousa Khani, M., Zaretalab, A. (2014). Comparing Parallel Simulated Annealing, Parallel Vibrating Damp Optimization and Genetic Algorithm for Joint Redundancy-Availability Problems in a Series-Parallel System with Multi-State Components. Journal of Optimization in Industrial Engineering, 7(14), 13-26.
Mani Sharifi; Morteza Mousa Khani; Arash Zaretalab. "Comparing Parallel Simulated Annealing, Parallel Vibrating Damp Optimization and Genetic Algorithm for Joint Redundancy-Availability Problems in a Series-Parallel System with Multi-State Components". Journal of Optimization in Industrial Engineering, 7, 14, 2014, 13-26.
Sharifi, M., Mousa Khani, M., Zaretalab, A. (2014). 'Comparing Parallel Simulated Annealing, Parallel Vibrating Damp Optimization and Genetic Algorithm for Joint Redundancy-Availability Problems in a Series-Parallel System with Multi-State Components', Journal of Optimization in Industrial Engineering, 7(14), pp. 13-26.
Sharifi, M., Mousa Khani, M., Zaretalab, A. Comparing Parallel Simulated Annealing, Parallel Vibrating Damp Optimization and Genetic Algorithm for Joint Redundancy-Availability Problems in a Series-Parallel System with Multi-State Components. Journal of Optimization in Industrial Engineering, 2014; 7(14): 13-26.
Comparing Parallel Simulated Annealing, Parallel Vibrating Damp Optimization and Genetic Algorithm for Joint Redundancy-Availability Problems in a Series-Parallel System with Multi-State Components
1Assistant Professor, Faculty of Industrial & Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2Associate Professor, Faculty of Management & Accounting, Qazvin Branch, Islamic Azad University, Qazvin, Iran
3MSc, Faculty of Industrial & Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Abstract
In this paper, we study different methods of solving joint redundancy-availability optimization for series-parallel systems with multi-state components. We analyzed various effective factors on system availability in order to determine the optimum number and version of components in each sub-system and consider the effects of improving failure rates of each component in each sub-system and improving reliability of each sub-system. The target is to determine optimum values of all variables for improving the availability level and decreasing the total cost of the system. At first, the exact values of variables are determined using a mathematical model; then, the results of SA-Parallel, VDO-Parallel and genetic algorithms are compared with the exact solution.
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