Optimization of Automotive Manufacturing Layout for Productivity Improvement
This paper deal with an optimization of automotive manufacturing layout by using meta-heuristics approach aided with discrete event simulation (WITNESS Simulation). The objective of this study is to balance the workload, increase line efficiency, and improve productivity by...
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| 主要な著者: | , , |
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| フォーマット: | 論文 |
| 言語: | English English |
| 出版事項: |
Penerbit UiTM
2017
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| 主題: | |
| オンライン･アクセス: | http://umpir.ump.edu.my/id/eprint/18479/1/2017%20Magffierah%20Opt%20of%20Automotive%20Layout.pdf http://umpir.ump.edu.my/id/eprint/18479/7/fkm-2017-fadzil-Optimization%20of%20Automotive%20Manufacturing.pdf http://umpir.ump.edu.my/id/eprint/18479/ https://jmeche.uitm.edu.my/index.php/home/journal/special-issues/engineering-for-humanity/170-optimization-of-automotive-manufacturing-layout-for-productivity-improvement |
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| 要約: | This paper deal with an optimization of automotive manufacturing layout by using meta-heuristics approach aided with discrete event simulation (WITNESS Simulation). The objective of this study is to balance the workload, increase line efficiency, and improve productivity by optimizing assembly line balancing (ALB) using Genetic Algorithm. The current assembly line layout operated under the circumstance where idle time is high due to unbalance workload. After the optimization process takes place, the workload distribution in each workstation has shown a significant improvement. Furthermore, productivity improvement was gained after the
optimization followed by increment in term of line efficiency by 18%. In addition, the number of workstation needed to assemble the product can be
reduced from current layout (17 workstations) to an improved layout (14 workstations). The current study contributes to the implementation of Genetic
Algorithm in ALB to improve productivity of related automotive manufacturing industry. |
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