Assembly sequence planning using hybrid binary particle swarm optimization

Assembly Sequence Planning (ASP) is known as a large-scale, timeconsuming combinatorial problem. Therefore time is the main factor in production planning. Recently, ASP in production planning had been studied widely especially to minimize the time and consequently reduce the cost. The first objectiv...

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Main Author:	Ahmed Mukred, Jameel Abdulla
Format:	Thesis
Language:	English
Published:	2014
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/78088/1/JameelAbdullaAhmedPFKE2014.pdf
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id	my-utm-ep.78088
record_format	uketd_dc
spelling	my-utm-ep.780882018-07-23T06:10:23Z Assembly sequence planning using hybrid binary particle swarm optimization 2014-12 Ahmed Mukred, Jameel Abdulla TK Electrical engineering. Electronics Nuclear engineering Assembly Sequence Planning (ASP) is known as a large-scale, timeconsuming combinatorial problem. Therefore time is the main factor in production planning. Recently, ASP in production planning had been studied widely especially to minimize the time and consequently reduce the cost. The first objective of this research is to formulate and analyse a mathematical model of the ASP problem. The second objective is to minimize the time of the ASP problem and hence reduce the product cost. A case study of a product consists of 19 components have been used in this research, and the fitness function of the problem had been calculated using Binary Particle Swarm Optimization (BPSO), and hybrid algorithm of BPSO and Differential Evolution (DE). The novel algorithm of BPSODE has been assessed with performance-evaluated criteria (performance measure). The algorithm has been validated using 8 comprehensive benchmark problems from the literature. The results show that the BPSO algorithm has an improved performance and can reduce further the time of assembly of the 19 parts of the ASP compared to the Simulated Annealing and Genetic Algorithm. The novel hybrid BPSODE algorithm shows a superior performance when assessed via performance-evaluated criteria compared to BPSO. The BPSODE algorithm also demonstrated a good generation of the recorded optimal value for the 8 standard benchmark problems. 2014-12 Thesis http://eprints.utm.my/id/eprint/78088/ http://eprints.utm.my/id/eprint/78088/1/JameelAbdullaAhmedPFKE2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:98313 phd doctoral Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Ahmed Mukred, Jameel Abdulla Assembly sequence planning using hybrid binary particle swarm optimization
description	Assembly Sequence Planning (ASP) is known as a large-scale, timeconsuming combinatorial problem. Therefore time is the main factor in production planning. Recently, ASP in production planning had been studied widely especially to minimize the time and consequently reduce the cost. The first objective of this research is to formulate and analyse a mathematical model of the ASP problem. The second objective is to minimize the time of the ASP problem and hence reduce the product cost. A case study of a product consists of 19 components have been used in this research, and the fitness function of the problem had been calculated using Binary Particle Swarm Optimization (BPSO), and hybrid algorithm of BPSO and Differential Evolution (DE). The novel algorithm of BPSODE has been assessed with performance-evaluated criteria (performance measure). The algorithm has been validated using 8 comprehensive benchmark problems from the literature. The results show that the BPSO algorithm has an improved performance and can reduce further the time of assembly of the 19 parts of the ASP compared to the Simulated Annealing and Genetic Algorithm. The novel hybrid BPSODE algorithm shows a superior performance when assessed via performance-evaluated criteria compared to BPSO. The BPSODE algorithm also demonstrated a good generation of the recorded optimal value for the 8 standard benchmark problems.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Ahmed Mukred, Jameel Abdulla
author_facet	Ahmed Mukred, Jameel Abdulla
author_sort	Ahmed Mukred, Jameel Abdulla
title	Assembly sequence planning using hybrid binary particle swarm optimization
title_short	Assembly sequence planning using hybrid binary particle swarm optimization
title_full	Assembly sequence planning using hybrid binary particle swarm optimization
title_fullStr	Assembly sequence planning using hybrid binary particle swarm optimization
title_full_unstemmed	Assembly sequence planning using hybrid binary particle swarm optimization
title_sort	assembly sequence planning using hybrid binary particle swarm optimization
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2014
url	http://eprints.utm.my/id/eprint/78088/1/JameelAbdullaAhmedPFKE2014.pdf
_version_	1747817904053682176

Assembly sequence planning using hybrid binary particle swarm optimization

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