A New Hybrid Method for Redundancy Allocation in Production Systems using Modified NSGA-II and MOPSO Algorithm

This paper presents a multi-objective mathematical model for redundancy allocation in production systems. In many of the production and assembly lines, process times, time between failures and repaired times are generally distributed. The proposed method of this paper is able to consider time depend...

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Main Author: Ali Mohtashami
Format: Article
Language:fas
Published: Allameh Tabataba'i University Press 2015-01-01
Series:Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī
Subjects:
Online Access:http://jims.atu.ac.ir/article_588_6d3787d7180bae8a26c77a62c801d1cd.pdf
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spelling doaj-9bb5c89fa00f4783943b12a949ff8ec62020-11-25T00:13:46ZfasAllameh Tabataba'i University PressMuṭāli̒āt-i Mudīriyyat-i Ṣan̒atī2251-80292015-01-01123397124A New Hybrid Method for Redundancy Allocation in Production Systems using Modified NSGA-II and MOPSO AlgorithmAli MohtashamiThis paper presents a multi-objective mathematical model for redundancy allocation in production systems. In many of the production and assembly lines, process times, time between failures and repaired times are generally distributed. The proposed method of this paper is able to consider time dependent parameters as general distribution functions by using the hybrid approach of simulation and response surface methodology. The objectives of the mathematical model are maximizing production rate, minimizing total cost and maximizing quality. In order to solve the proposed mathematical model, non-dominated sorting genetic algorithm and multiple objective particle swarm optimization are used. Numerical results indicate the effectiveness of both algorithms for generating non-dominated solutions. Moreover, comparative results indicate the superiority of the Non-dominated sorting genetic algorithm. http://jims.atu.ac.ir/article_588_6d3787d7180bae8a26c77a62c801d1cd.pdfProduction line; Response Surface Methodology; Simulation; Non-dominated sorting genetic algorithm; Multiple objective particle swarm optimization
collection DOAJ
language fas
format Article
sources DOAJ
author Ali Mohtashami
spellingShingle Ali Mohtashami
A New Hybrid Method for Redundancy Allocation in Production Systems using Modified NSGA-II and MOPSO Algorithm
Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī
Production line; Response Surface Methodology; Simulation; Non-dominated sorting genetic algorithm; Multiple objective particle swarm optimization
author_facet Ali Mohtashami
author_sort Ali Mohtashami
title A New Hybrid Method for Redundancy Allocation in Production Systems using Modified NSGA-II and MOPSO Algorithm
title_short A New Hybrid Method for Redundancy Allocation in Production Systems using Modified NSGA-II and MOPSO Algorithm
title_full A New Hybrid Method for Redundancy Allocation in Production Systems using Modified NSGA-II and MOPSO Algorithm
title_fullStr A New Hybrid Method for Redundancy Allocation in Production Systems using Modified NSGA-II and MOPSO Algorithm
title_full_unstemmed A New Hybrid Method for Redundancy Allocation in Production Systems using Modified NSGA-II and MOPSO Algorithm
title_sort new hybrid method for redundancy allocation in production systems using modified nsga-ii and mopso algorithm
publisher Allameh Tabataba'i University Press
series Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī
issn 2251-8029
publishDate 2015-01-01
description This paper presents a multi-objective mathematical model for redundancy allocation in production systems. In many of the production and assembly lines, process times, time between failures and repaired times are generally distributed. The proposed method of this paper is able to consider time dependent parameters as general distribution functions by using the hybrid approach of simulation and response surface methodology. The objectives of the mathematical model are maximizing production rate, minimizing total cost and maximizing quality. In order to solve the proposed mathematical model, non-dominated sorting genetic algorithm and multiple objective particle swarm optimization are used. Numerical results indicate the effectiveness of both algorithms for generating non-dominated solutions. Moreover, comparative results indicate the superiority of the Non-dominated sorting genetic algorithm.
topic Production line; Response Surface Methodology; Simulation; Non-dominated sorting genetic algorithm; Multiple objective particle swarm optimization
url http://jims.atu.ac.ir/article_588_6d3787d7180bae8a26c77a62c801d1cd.pdf
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