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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Main Authors:	Muhamad Magffierah, Razali, M. F. F., Ab Rashid, Muhammad Razif, Abdullah Make
Format:	Article
Language:	English English
Published:	Penerbit UiTM 2017
Subjects:	TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/18479/ http://umpir.ump.edu.my/id/eprint/18479/ 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

id	ump-18479
recordtype	eprints
spelling	ump-184792018-07-27T02:16:56Z http://umpir.ump.edu.my/id/eprint/18479/ Optimization of Automotive Manufacturing Layout for Productivity Improvement Muhamad Magffierah, Razali M. F. F., Ab Rashid Muhammad Razif, Abdullah Make TS Manufactures 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. Penerbit UiTM 2017-08 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/18479/1/2017%20Magffierah%20Opt%20of%20Automotive%20Layout.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/18479/7/fkm-2017-fadzil-Optimization%20of%20Automotive%20Manufacturing.pdf Muhamad Magffierah, Razali and M. F. F., Ab Rashid and Muhammad Razif, Abdullah Make (2017) Optimization of Automotive Manufacturing Layout for Productivity Improvement. Journal of Mechanical Engineering , SI 4 (1). pp. 171-184. ISSN 1823- 5514(print); 2550-164X(online) https://jmeche.uitm.edu.my/index.php/home/journal/special-issues/engineering-for-humanity/170-optimization-of-automotive-manufacturing-layout-for-productivity-improvement
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	TS Manufactures
spellingShingle	TS Manufactures Muhamad Magffierah, Razali M. F. F., Ab Rashid Muhammad Razif, Abdullah Make Optimization of Automotive Manufacturing Layout for Productivity Improvement
description	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.
format	Article
author	Muhamad Magffierah, Razali M. F. F., Ab Rashid Muhammad Razif, Abdullah Make
author_facet	Muhamad Magffierah, Razali M. F. F., Ab Rashid Muhammad Razif, Abdullah Make
author_sort	Muhamad Magffierah, Razali
title	Optimization of Automotive Manufacturing Layout for Productivity Improvement
title_short	Optimization of Automotive Manufacturing Layout for Productivity Improvement
title_full	Optimization of Automotive Manufacturing Layout for Productivity Improvement
title_fullStr	Optimization of Automotive Manufacturing Layout for Productivity Improvement
title_full_unstemmed	Optimization of Automotive Manufacturing Layout for Productivity Improvement
title_sort	optimization of automotive manufacturing layout for productivity improvement
publisher	Penerbit UiTM
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/18479/ http://umpir.ump.edu.my/id/eprint/18479/ 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
first_indexed	2023-09-18T22:26:12Z
last_indexed	2023-09-18T22:26:12Z
_version_	1777415983018803200

Optimization of Automotive Manufacturing Layout for Productivity Improvement

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