Optimization of Automotive Manufacturing Layout for Productivity Improvement / Muhamad Magffierah Razali ...[et al.]

Razali, Muhamad Magffierah and Ab.Rashid, Mohd Fadzil Faisae and Abdullah Make, Muhammad Razif (2017) Optimization of Automotive Manufacturing Layout for Productivity Improvement / Muhamad Magffierah Razali ...[et al.]. Journal of Mechanical Engineering (JMechE), SI 4 (1). pp. 171-184. ISSN 18235514

Abstract

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.

Metadata

Item Type: Article
Creators:
CreatorsEmail / ID. Num
Razali, Muhamad MagffierahUNSPECIFIED
Ab.Rashid, Mohd Fadzil FaisaeUNSPECIFIED
Abdullah Make, Muhammad RazifUNSPECIFIED
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Mechanical Engineering
Journal or Publication Title: Journal of Mechanical Engineering (JMechE)
Journal: UiTM Journal > Journal of Mechanical Engineering (JMechE)
ISSN: 18235514
Volume: SI 4
Number: 1
Page Range: pp. 171-184
Item ID: 39258
Uncontrolled Keywords: Assembly Line Balancing, Genetic Algorithm, Productivity Improvement
URI: http://ir.uitm.edu.my/id/eprint/39258

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