Modelling and optimization of energy efficient assemblyline balancing using modified moth flame optimizer

Energy utilization is a global issue due to the reduction of fossil resources and also negative environmental effect. The assembly process in the manufacturing sector needs to move to a new dimension by taking into account energy utilization when designing the assembly line. Recently, researchers st...

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Bibliographic Details
Main Authors: Mohd Fadzil Faisae, Ab Rashid, Nik Mohd Zuki, Nik Mohamed, Oumer, Ahmed Nurye
Format: Article
Language:English
Published: Penerbit UTHM 2022
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/35148/1/Modelling%20and%20optimization%20of%20energy%20efficient%20assembly%20line%20balancing%20using%20modified%20moth%20flame%20optimizer.pdf
http://umpir.ump.edu.my/id/eprint/35148/
https://doi.org/10.30880/ijie.2022.14.01.003
https://doi.org/10.30880/ijie.2022.14.01.003
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Summary:Energy utilization is a global issue due to the reduction of fossil resources and also negative environmental effect. The assembly process in the manufacturing sector needs to move to a new dimension by taking into account energy utilization when designing the assembly line. Recently, researchers studied assembly line balancing (ALB) by considering energy utilization. However, the current works were limited to robotic assembly line problem. This work has proposed a model of energy efficient ALB (EE-ALB) and optimize the problem using a new modified moth flame optimizer (MMFO). The MMFO introduces the best flame concept to guide the global search direction. The proposed MMFO is tested by using 34 cases from benchmark problems. The numerical experiment results showed that the proposed MMFO, in general, is able to optimize the EE-ALB problem better compared to five comparison algorithms within reasonable computational time. Statistical test indicated that the MMFO has a significant performance in 75% of the cases. The proposed model can be a guideline for manufacturer to set up a green assembly line in future.