A value-driven approach for optimizing reliability-redundancy allocation problem in multi-state weighted k-out-of-n system. (July 2016)

• Record Type: Journal Article
• Authors: Khorshidi, Hadi Akbarzade; Gunawan, Indra; Ibrahim, M. Yousef
• Is Part Of: Journal of manufacturing systems. Volume 40(2016)Part 1
• Page Start: 54
• Page End: 62
• Publication Date: 2016-07
• Subjects: Multi-state weighted k-out-of-n system -- Net present value -- System reliability optimization -- Manufacturing system -- Universal generating function; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2016.06.002
• Languages: English
• ISSNs: 0278-6125

Abstract

Graphical abstract: Highlights: A new integer nonlinear optimization model is developed for reliability-redundancy allocation problem. The present value is used to estimate the system reliability of multi-state weighted k -out-of- n systems. Net present value combines two objective functions as system reliability and system cost. Decision making on maintenance actions during functioning periods of a manufacturing system. Genetic algorithm has been utilized to solve the optimization problem. Abstract: This study concentrates on optimizing a multi-state weighted k -out-of- n system consisting of different types of reparable components. An optimization model is developed to decide upon both optimal redundancy allocation and best maintenance activities for reliability allocation. The optimization model is to maximize system reliability and minimize system cost simultaneously. To reach this goal, a financial view has been employed to estimate the system reliability, and combine it with system cost. The proposed model determines the number of each component type in the system, and the maintenance actions and the time at which they should be applied. Also, the model is programmed by MATLAB, and Genetic Algorithm is used to solve a practical case.