Grouping technology and a hybrid genetic algorithm‐desirability function approach for optimum design of cellular manufacturing systems
Abstract Cell formation and machine layout in cellular manufacturing systems (CMs) design are considered as a crucial, yet hard and complex decision process. Owing to the nondeterministic polynomial time (NP) and combinatorial class of this problem, this paper presents an innovative heuristic approa...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-12-01
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| Series: | IET Collaborative Intelligent Manufacturing |
| Online Access: | https://doi.org/10.1049/cim2.12053 |
| _version_ | 1797980912377397248 |
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| author | Atiya Al‐Zuheri Hussein S. Ketan Ilias Vlachos |
| author_facet | Atiya Al‐Zuheri Hussein S. Ketan Ilias Vlachos |
| author_sort | Atiya Al‐Zuheri |
| collection | DOAJ |
| description | Abstract Cell formation and machine layout in cellular manufacturing systems (CMs) design are considered as a crucial, yet hard and complex decision process. Owing to the nondeterministic polynomial time (NP) and combinatorial class of this problem, this paper presents an innovative heuristic approach to re‐arrange machines enabling the minimisation of inter/intra‐ cellular movements as well as the cost of material handling between machines, therefore increasing group efficiency and efficacy. The heuristic approach, which is based on group technology, genetic algorithms, and desirability function, determines the optimal solution for flexible cell formation and machine layout within each cell. Flexibility refers to an explicit improvement using the desirability function to modify cell design by altering the ratio data; that is, the weight factor to meet demand flexibility. Specifically, the desirable function proposed here to provide the optimal setting of the weighting factor as a key factor which enables CMs design the flexibility to control the cell size. Promised results were obtained when the proposed approach was applied to a case study. Practical implications and recommendations are provided for use by decision makers in the design of CMs. |
| first_indexed | 2024-04-11T06:02:13Z |
| format | Article |
| id | doaj.art-a45589dcfa9240da9e3ee7351806eb28 |
| institution | Directory Open Access Journal |
| issn | 2516-8398 |
| language | English |
| last_indexed | 2024-04-11T06:02:13Z |
| publishDate | 2022-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Collaborative Intelligent Manufacturing |
| spelling | doaj.art-a45589dcfa9240da9e3ee7351806eb282022-12-22T04:41:39ZengWileyIET Collaborative Intelligent Manufacturing2516-83982022-12-014426728510.1049/cim2.12053Grouping technology and a hybrid genetic algorithm‐desirability function approach for optimum design of cellular manufacturing systemsAtiya Al‐Zuheri0Hussein S. Ketan1Ilias Vlachos2Department of Production Engineering and Metallurgy University of Technology Baghdad IraqCollege of Engineering University of Warith Al Anbiyaa Karbala IraqLa Rochelle Business School Excelia Group La Rochelle FranceAbstract Cell formation and machine layout in cellular manufacturing systems (CMs) design are considered as a crucial, yet hard and complex decision process. Owing to the nondeterministic polynomial time (NP) and combinatorial class of this problem, this paper presents an innovative heuristic approach to re‐arrange machines enabling the minimisation of inter/intra‐ cellular movements as well as the cost of material handling between machines, therefore increasing group efficiency and efficacy. The heuristic approach, which is based on group technology, genetic algorithms, and desirability function, determines the optimal solution for flexible cell formation and machine layout within each cell. Flexibility refers to an explicit improvement using the desirability function to modify cell design by altering the ratio data; that is, the weight factor to meet demand flexibility. Specifically, the desirable function proposed here to provide the optimal setting of the weighting factor as a key factor which enables CMs design the flexibility to control the cell size. Promised results were obtained when the proposed approach was applied to a case study. Practical implications and recommendations are provided for use by decision makers in the design of CMs.https://doi.org/10.1049/cim2.12053 |
| spellingShingle | Atiya Al‐Zuheri Hussein S. Ketan Ilias Vlachos Grouping technology and a hybrid genetic algorithm‐desirability function approach for optimum design of cellular manufacturing systems IET Collaborative Intelligent Manufacturing |
| title | Grouping technology and a hybrid genetic algorithm‐desirability function approach for optimum design of cellular manufacturing systems |
| title_full | Grouping technology and a hybrid genetic algorithm‐desirability function approach for optimum design of cellular manufacturing systems |
| title_fullStr | Grouping technology and a hybrid genetic algorithm‐desirability function approach for optimum design of cellular manufacturing systems |
| title_full_unstemmed | Grouping technology and a hybrid genetic algorithm‐desirability function approach for optimum design of cellular manufacturing systems |
| title_short | Grouping technology and a hybrid genetic algorithm‐desirability function approach for optimum design of cellular manufacturing systems |
| title_sort | grouping technology and a hybrid genetic algorithm desirability function approach for optimum design of cellular manufacturing systems |
| url | https://doi.org/10.1049/cim2.12053 |
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