Soft Computing Methodology to Optimize the Integrated Dynamic Models of Cellular Manufacturing Systems in a Robust Environment

Machine learning, neural networks, and metaheuristic algorithms are relatively new subjects, closely related to each other: learning is somehow an intrinsic part of all of them. On the other hand, cell formation (CF) and facility layout design are the two fundamental steps in the CMS implementation....

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Amir-Mohammad Golmohammadi, Hasan Rasay, Zaynab Akhoundpour Amiri, Maryam Solgi, Negar Balajeh
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/656272236aae471185d8840efa8ca9d6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:656272236aae471185d8840efa8ca9d6
record_format	dspace
spelling	oai:doaj.org-article:656272236aae471185d8840efa8ca9d62021-11-22T01:11:17ZSoft Computing Methodology to Optimize the Integrated Dynamic Models of Cellular Manufacturing Systems in a Robust Environment1563-514710.1155/2021/3040391https://doaj.org/article/656272236aae471185d8840efa8ca9d62021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3040391https://doaj.org/toc/1563-5147Machine learning, neural networks, and metaheuristic algorithms are relatively new subjects, closely related to each other: learning is somehow an intrinsic part of all of them. On the other hand, cell formation (CF) and facility layout design are the two fundamental steps in the CMS implementation. To get a successful CMS design, addressing the interrelated decisions simultaneously is important. In this article, a new nonlinear mixed-integer programming model is presented which comprehensively considers solving the integrated dynamic cell formation and inter/intracell layouts in continuous space. In the proposed model, cells are configured in flexible shapes during the planning horizon considering cell capacity in each period. This study considers the exact information about facility layout design and material handling cost. The proposed model is an NP-hard mixed-integer nonlinear programming model. To optimize the proposed problem, first, three metaheuristic algorithms, that is, Genetic Algorithm (GA), Keshtel Algorithm (KA), and Red Deer Algorithm (RDA), are employed. Then, to further improve the quality of the solutions, using machine learning approaches and combining the results of the aforementioned algorithms, a new metaheuristic algorithm is proposed. Numerical examples, sensitivity analyses, and comparisons of the performances of the algorithms are conducted.Amir-Mohammad GolmohammadiHasan RasayZaynab Akhoundpour AmiriMaryam SolgiNegar BalajehHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Amir-Mohammad Golmohammadi Hasan Rasay Zaynab Akhoundpour Amiri Maryam Solgi Negar Balajeh Soft Computing Methodology to Optimize the Integrated Dynamic Models of Cellular Manufacturing Systems in a Robust Environment
description	Machine learning, neural networks, and metaheuristic algorithms are relatively new subjects, closely related to each other: learning is somehow an intrinsic part of all of them. On the other hand, cell formation (CF) and facility layout design are the two fundamental steps in the CMS implementation. To get a successful CMS design, addressing the interrelated decisions simultaneously is important. In this article, a new nonlinear mixed-integer programming model is presented which comprehensively considers solving the integrated dynamic cell formation and inter/intracell layouts in continuous space. In the proposed model, cells are configured in flexible shapes during the planning horizon considering cell capacity in each period. This study considers the exact information about facility layout design and material handling cost. The proposed model is an NP-hard mixed-integer nonlinear programming model. To optimize the proposed problem, first, three metaheuristic algorithms, that is, Genetic Algorithm (GA), Keshtel Algorithm (KA), and Red Deer Algorithm (RDA), are employed. Then, to further improve the quality of the solutions, using machine learning approaches and combining the results of the aforementioned algorithms, a new metaheuristic algorithm is proposed. Numerical examples, sensitivity analyses, and comparisons of the performances of the algorithms are conducted.
format	article
author	Amir-Mohammad Golmohammadi Hasan Rasay Zaynab Akhoundpour Amiri Maryam Solgi Negar Balajeh
author_facet	Amir-Mohammad Golmohammadi Hasan Rasay Zaynab Akhoundpour Amiri Maryam Solgi Negar Balajeh
author_sort	Amir-Mohammad Golmohammadi
title	Soft Computing Methodology to Optimize the Integrated Dynamic Models of Cellular Manufacturing Systems in a Robust Environment
title_short	Soft Computing Methodology to Optimize the Integrated Dynamic Models of Cellular Manufacturing Systems in a Robust Environment
title_full	Soft Computing Methodology to Optimize the Integrated Dynamic Models of Cellular Manufacturing Systems in a Robust Environment
title_fullStr	Soft Computing Methodology to Optimize the Integrated Dynamic Models of Cellular Manufacturing Systems in a Robust Environment
title_full_unstemmed	Soft Computing Methodology to Optimize the Integrated Dynamic Models of Cellular Manufacturing Systems in a Robust Environment
title_sort	soft computing methodology to optimize the integrated dynamic models of cellular manufacturing systems in a robust environment
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/656272236aae471185d8840efa8ca9d6
work_keys_str_mv	AT amirmohammadgolmohammadi softcomputingmethodologytooptimizetheintegrateddynamicmodelsofcellularmanufacturingsystemsinarobustenvironment AT hasanrasay softcomputingmethodologytooptimizetheintegrateddynamicmodelsofcellularmanufacturingsystemsinarobustenvironment AT zaynabakhoundpouramiri softcomputingmethodologytooptimizetheintegrateddynamicmodelsofcellularmanufacturingsystemsinarobustenvironment AT maryamsolgi softcomputingmethodologytooptimizetheintegrateddynamicmodelsofcellularmanufacturingsystemsinarobustenvironment AT negarbalajeh softcomputingmethodologytooptimizetheintegrateddynamicmodelsofcellularmanufacturingsystemsinarobustenvironment
_version_	1718418284406112256

Soft Computing Methodology to Optimize the Integrated Dynamic Models of Cellular Manufacturing Systems in a Robust Environment

Ejemplares similares