Intergrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation

In assembly optimisation, Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) optimisations currently performed in serial, present an opportunity for integration, allowing benefits such as larger search space leading to better solution quality, reduced error rate in planning and fast...

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Main Author:	M. F. F., Ab Rashid
Format:	Thesis
Language:	English
Published:	2013
Subjects:	QA Mathematics QC Physics
Online Access:	http://umpir.ump.edu.my/id/eprint/13473/1/MOHD%20FADZIL%20FAISAE%20AB%20RASHID.pdf
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id	my-ump-ir.13473
record_format	uketd_dc
spelling	my-ump-ir.134732021-08-19T05:32:01Z Intergrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation 2013 M. F. F., Ab Rashid QA Mathematics QC Physics In assembly optimisation, Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) optimisations currently performed in serial, present an opportunity for integration, allowing benefits such as larger search space leading to better solution quality, reduced error rate in planning and fast time-tomarket for a product. The literature survey highlights the research gaps, where the existing integrated ASP and ALB optimisation is limited to a Genetic Algorithm (GA) based approach, while Particle Swarm Optimisation (PSO) demonstrates better performance in individual ASP and ALB optimization compared to GA In addition, the existing works are limited to simple assembly line problems which run a homogeneous model on an assembly line. The aim of this research is to establish a methodology and algorithm for integrating ASP and ALB optimisation using Particle Swarm Optimisation. This research extends the problem type to integrated mixed-model ASP and ALB in order to generalize the problem. This research proposes Multi-Objective Discrete Particle Swarm Optimisation (MODPSO), to optimise integrated ASP and ALB. The MODPSO uses the Pareto-based approach to deal with the multi-objective problem and adopts a discrete procedure instead of standard mathematical operators to update its position and velocity. The MODPSO algorithm is tested with a wide range of problem difficulties for integrated single-model and mixed-model ASP and ALB problems. In order to supply sufficient test problems that cover a range of problem difficulties, a tuneable test problem generator is developed. Statistical tests on the algorithms' performance indicates that the proposed MODPSO algorithm presents significant improvement in terms of larger nondominated solution numbers in Pare't o optimal, compared to comparable algorithms including GA based algorithms in both single-model and mixedmodel ASP and ALB problems. The performance of the MODPSO algorithm is finally validated using artificial problems from the literature and real-world problems from assembly products. 2013 Thesis http://umpir.ump.edu.my/id/eprint/13473/ http://umpir.ump.edu.my/id/eprint/13473/1/MOHD%20FADZIL%20FAISAE%20AB%20RASHID.pdf application/pdf en public phd doctoral University of Cranfield Faculty of Industrial Sciences and Technology
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
topic	QA Mathematics QC Physics
spellingShingle	QA Mathematics QC Physics M. F. F., Ab Rashid Intergrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation
description	In assembly optimisation, Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) optimisations currently performed in serial, present an opportunity for integration, allowing benefits such as larger search space leading to better solution quality, reduced error rate in planning and fast time-tomarket for a product. The literature survey highlights the research gaps, where the existing integrated ASP and ALB optimisation is limited to a Genetic Algorithm (GA) based approach, while Particle Swarm Optimisation (PSO) demonstrates better performance in individual ASP and ALB optimization compared to GA In addition, the existing works are limited to simple assembly line problems which run a homogeneous model on an assembly line. The aim of this research is to establish a methodology and algorithm for integrating ASP and ALB optimisation using Particle Swarm Optimisation. This research extends the problem type to integrated mixed-model ASP and ALB in order to generalize the problem. This research proposes Multi-Objective Discrete Particle Swarm Optimisation (MODPSO), to optimise integrated ASP and ALB. The MODPSO uses the Pareto-based approach to deal with the multi-objective problem and adopts a discrete procedure instead of standard mathematical operators to update its position and velocity. The MODPSO algorithm is tested with a wide range of problem difficulties for integrated single-model and mixed-model ASP and ALB problems. In order to supply sufficient test problems that cover a range of problem difficulties, a tuneable test problem generator is developed. Statistical tests on the algorithms' performance indicates that the proposed MODPSO algorithm presents significant improvement in terms of larger nondominated solution numbers in Pare't o optimal, compared to comparable algorithms including GA based algorithms in both single-model and mixedmodel ASP and ALB problems. The performance of the MODPSO algorithm is finally validated using artificial problems from the literature and real-world problems from assembly products.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	M. F. F., Ab Rashid
author_facet	M. F. F., Ab Rashid
author_sort	M. F. F., Ab Rashid
title	Intergrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation
title_short	Intergrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation
title_full	Intergrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation
title_fullStr	Intergrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation
title_full_unstemmed	Intergrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation
title_sort	intergrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation
granting_institution	University of Cranfield
granting_department	Faculty of Industrial Sciences and Technology
publishDate	2013
url	http://umpir.ump.edu.my/id/eprint/13473/1/MOHD%20FADZIL%20FAISAE%20AB%20RASHID.pdf
_version_	1783731984276652032

Intergrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation

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