Volume 21, Issue 4 (12-2024)                   jor 2024, 21(4): 117-133 | Back to browse issues page

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Shahriari M, Najjari F. Design and Implementation of a Multi-objective Model Using a Genetic Algorithm for Optimizing Redundancy Allocation Problems in Reliable Systems. jor 2024; 21 (4) :117-133
URL: http://jamlu.liau.ac.ir/article-1-2263-en.html
Design and Implementation of a Multi-objective Model Using a Genetic Algorithm for Optimizing Redundancy Allocation Problems in Reliable Systems
M. Shahriari * , F. Najjari
Department of Industrial Management, Faculty of Management, Islamic Azad University, South Tehran Branch, Tehran, Iran , shahriari.mr@gmail.com
Abstract:   (526 Views)
In enhancing the reliability of systems, the redundancy allocation problem emerges as a direct avenue in the initial product design phase. These problems involve various components with different parameters such as cost, weight, and volume, aiming to allocate a number of different types of components to each subsystem in a way that optimizes the objective function. Due to the complexity of these problems, traditional optimization methods may not be feasible. To address this challenge, the utilization of metaheuristic approaches such as the genetic algorithm is necessary, leading to the generation of an optimal Pareto set. Two methods are employed to reduce the volume of solution sets. The first method involves pruning the solution set through non-numeric preference ranking, assisting the decision-maker in selecting solutions based on prioritization. The second method employs data clustering techniques to group similar data points into clusters. The k-means algorithm is utilized to present this technique, providing a general solution for presentation to the decision-maker.
 
Keywords: System Reliability, Redundancy Allocation, Multi-Objective Genetic Algorithm, Set of Pareto Optimal Solutions, Clustering Analysis.
Full-Text [PDF 1026 kb]   (111 Downloads)    
Type of Study: Research | Subject: Special
Received: 2023/12/23 | Accepted: 2024/05/19 | Published: 2024/12/21
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