Availability Optimization of a system With k-out-of-n Sub-systems Considering Different Types of Components Failure Using BBQ Algorithm
محورهای موضوعی : مجله بین المللی ریاضیات صنعتیM. Sharifi 1 , MR. Shahriari 2 , Sh. Khoshniat 3
1 - Department of Industrial & Mechanical Engineering,
Qazvin Branch, Islamic Azad University, Qazvin, Iran.
2 - Department of Management & Accounting, South
Tehran Branch, Islamic Azad University, Tehran, Iran.
3 - Department of Industrial & Mechanical Engineering,
Qazvin Branch, Islamic Azad University, Qazvin, Iran.
کلید واژه: Common cause failure, Biographic Based Optimization algorithm, availability, Short Circuit, Repairable,
چکیده مقاله :
Redundancy allocation problem is one of the most important problem in Reliability area. In this problem the reliability and availability of the systems maximized via allocating redundant components to sub-systems. a systems operates normally in its operational mode but fails in either opened or shorted modes. this paper presents a repairable k_out_of_n systems network model with common cause failures. We used Biographic Based Optimization algorithm for solving the presented problem.
Redundancy allocation problem is one of the most important problems in reliability field. In this problem, the reliability and availability of the systems are maximized via allocating redundant components to subsystems. Many different assumptions are considered to draw this problem near to real conditions. In this paper, we work on a system with k-out-o-n subsystems as well as considering short circuit and common cause failures for the components in each subs in addition to ordinary components failures. Obviously, the components are repairable. We present a Markov model to show the effects of these two failures on system availability. For solving the presented model, we used Biographic Based Optimization (BBO) algorithm and minimize the system cost to achieve the predetermined system availability. We used the BBO algorithm for calculating the availability of the system, and response surface methodology for tuning the algorithm parameters.
