A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing

Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) are traditionally optimised independently. However recently, integrated ASP and ALB optimisation has become more relevant to obtain better quality solution and to reduce time to market. Despite many optimisation algorithms that were...

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Main Authors:	M. F. F., Ab Rashid, N. M. Z., Nik Mohamed, A. N. M., Rose
Format:	Article
Language:	English
Published:	UMP Press 2019
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/27433/ http://umpir.ump.edu.my/id/eprint/27433/ http://umpir.ump.edu.my/id/eprint/27433/1/A%20modified%20artificial%20bee%20colony%20algorithm.pdf

id	ump-27433
recordtype	eprints
spelling	ump-274332020-02-03T04:23:50Z http://umpir.ump.edu.my/id/eprint/27433/ A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing M. F. F., Ab Rashid N. M. Z., Nik Mohamed A. N. M., Rose TJ Mechanical engineering and machinery Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) are traditionally optimised independently. However recently, integrated ASP and ALB optimisation has become more relevant to obtain better quality solution and to reduce time to market. Despite many optimisation algorithms that were proposed to optimise this problem, the existing researches on this problem were limited to Evolutionary Algorithm (EA), Ant Colony Optimisation (ACO), and Particle Swarm Optimisation (PSO). This paper proposed a modified Artificial Bee Colony algorithm (MABC) to optimise the integrated ASP and ALB problem. The proposed algorithm adopts beewolves predatory concept from Grey Wolf Optimiser to improve the exploitation ability in Artificial Bee Colony (ABC) algorithm. The proposed MABC was tested with a set of benchmark problems. The results indicated that the MABC outperformed the comparison algorithms in 91% of the benchmark problems. Furthermore, a statistical test reported that the MABC had significant performances in 80% of the cases. UMP Press 2019 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/27433/1/A%20modified%20artificial%20bee%20colony%20algorithm.pdf M. F. F., Ab Rashid and N. M. Z., Nik Mohamed and A. N. M., Rose (2019) A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing. Journal of Mechanical Engineering and Sciences (JMES), 13 (4). pp. 5905-5921. ISSN 2289-4659 (print); 2231-8380 (online) http://journal.ump.edu.my/jmes/article/view/1928
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery M. F. F., Ab Rashid N. M. Z., Nik Mohamed A. N. M., Rose A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing
description	Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) are traditionally optimised independently. However recently, integrated ASP and ALB optimisation has become more relevant to obtain better quality solution and to reduce time to market. Despite many optimisation algorithms that were proposed to optimise this problem, the existing researches on this problem were limited to Evolutionary Algorithm (EA), Ant Colony Optimisation (ACO), and Particle Swarm Optimisation (PSO). This paper proposed a modified Artificial Bee Colony algorithm (MABC) to optimise the integrated ASP and ALB problem. The proposed algorithm adopts beewolves predatory concept from Grey Wolf Optimiser to improve the exploitation ability in Artificial Bee Colony (ABC) algorithm. The proposed MABC was tested with a set of benchmark problems. The results indicated that the MABC outperformed the comparison algorithms in 91% of the benchmark problems. Furthermore, a statistical test reported that the MABC had significant performances in 80% of the cases.
format	Article
author	M. F. F., Ab Rashid N. M. Z., Nik Mohamed A. N. M., Rose
author_facet	M. F. F., Ab Rashid N. M. Z., Nik Mohamed A. N. M., Rose
author_sort	M. F. F., Ab Rashid
title	A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing
title_short	A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing
title_full	A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing
title_fullStr	A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing
title_full_unstemmed	A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing
title_sort	modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing
publisher	UMP Press
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/27433/ http://umpir.ump.edu.my/id/eprint/27433/ http://umpir.ump.edu.my/id/eprint/27433/1/A%20modified%20artificial%20bee%20colony%20algorithm.pdf
first_indexed	2023-09-18T22:43:07Z
last_indexed	2023-09-18T22:43:07Z
_version_	1777417047597121536

A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing

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