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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Allameh Tabataba'i University Press
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Online Access: | http://jims.atu.ac.ir/article_588_6d3787d7180bae8a26c77a62c801d1cd.pdf |
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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.
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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 |
work_keys_str_mv |
AT alimohtashami anewhybridmethodforredundancyallocationinproductionsystemsusingmodifiednsgaiiandmopsoalgorithm AT alimohtashami newhybridmethodforredundancyallocationinproductionsystemsusingmodifiednsgaiiandmopsoalgorithm |
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1725393128699985920 |