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

M. F. F., Ab Rashid (2013) Intergrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation. PhD thesis, University of Cranfield (Contributors, UNSPECIFIED: UNSPECIFIED).

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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.

Item Type: Thesis (PhD)
Additional Information: Thesis (Doctor of Philosophy (Applied Sciences)) - University of Cranfield – 2013
Uncontrolled Keywords: Combinatorial optimization; Mathematical physics
Subjects: Q Science > QA Mathematics
Q Science > QC Physics
Faculty/Division: Faculty of Industrial Sciences And Technology
Depositing User: Ms. Nurul Amanina Lokman
Date Deposited: 23 Aug 2016 01:53
Last Modified: 19 Aug 2021 05:32
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