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....

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Veröffentlicht in:Mathematical problems in engineering Jg. 2021; S. 1 - 13
Hauptverfasser: Golmohammadi, Amir-Mohammad, Rasay, Hasan, Akhoundpour Amiri, Zaynab, Solgi, Maryam, Balajeh, Negar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Hindawi 11.11.2021
John Wiley & Sons, Inc
Schlagworte:
Algorithms
Cellular communication
Cellular manufacture
Decision theory
Dynamic models
Facilities management
Genetic algorithms
Heuristic methods
Industrial engineering
Integer programming
Job shops
Layouts
Machine learning
Materials handling
Mathematical models
Mixed integer
Neural networks
Nonlinear programming
Planning
Random variables
Robustness (mathematics)
Soft computing
ISSN:1024-123X, 1563-5147
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1024-123X
1563-5147
DOI:10.1155/2021/3040391