Browsing by Author "Ab Rashid, Mohd Fadzil Faisae"

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    Comparison of sequential and integrated optimisation approaches for ASP and ALB
    (Elsevier, 2017-07-11) Ab Rashid, Mohd Fadzil Faisae; Tiwari, Ashutosh; Hutabarat, Windo
    Combining Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) is now of increasing interest. The customary approach is the sequential approach, where ASP is optimised before ALB. Recently, interest in the integrated approach has begun to pick up. In an integrated approach, both ASP and ALB are optimised at the same time. Various claims have been made regarding the benefits of integrated optimisation compared with sequential optimisation, such as access to a larger search space that leads to better solution quality, reduced error rate in planning and expedited product time-to-market. These benefits are often cited but no existing work has substantiated the claimed benefits by publishing a quantitative comparison between sequential and integrated approaches. This paper therefore compares the sequential and integrated optimisation approaches for ASP and ALB using 51 test problems. This is done so that the behaviour of each approach in optimising ASP and ALB problems at different difficulty levels can be properly understood. An algorithm named Multi-Objective Discrete Particle Swarm Optimisation (MODPSO) is applied in both approaches. For ASP, the optimisation results indicate that the integrated approach is suitable to be used in small and medium-sized problems, according to the number of non-dominated solution and error ratio indicators. Meanwhile, the sequential approach converges more quickly in large-sized problems. For pure ALB, the integrated approach is preferable in all cases. When both ASP and ALB are considered, the integrated approach is superior to the sequential approach.
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    Development of a tuneable test problem generator for assembly sequence planning and assembly line balancing
    (SAGE Publications, 2012-11-30) Ab Rashid, Mohd Fadzil Faisae; Hutabarat, Windo; Tiwari, Ashutosh
    Assembly optimisation activities that involve assembly sequence planning and assembly line balancing have been extensively studied because of the importance of optimal assembly efficiency to manufacturing competitiveness. Numerous research works in assembly sequence planning and assembly line balancing mainly focus on developing algorithms to solve problems and to optimise assembly sequence planning and assembly line balancing. However, there is a scarcity in works that focus on developing problems to test these algorithms. In optimisation algorithm development, testing algorithms by a broad range of test problems is crucial to identify their strengths and weaknesses. This article proposes a generator of assembly sequence planning and assembly line balancing test problems with tuneable complexity levels. Experiments confirm that the selected combination of input attributes does control the generated assembly sequence planning and assembly line balancing problem complexity, and also that the generated problems can be used to identify the suitability of a given algorithm to problem types.
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    Integrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation
    (Cranfield University, 2013-09) Ab Rashid, Mohd Fadzil Faisae; Tiwari, Ashutosh
    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-to-market 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 optimisation 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 generalise 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 non-dominated solution numbers in Pareto optimal, compared to comparable algorithms including GA based algorithms in both single-model and mixed-model 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.
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    An integrated representation scheme for assembly sequence planning and assembly line balancing
    (Glasgow Caledonian University, 2011-12) Ab Rashid, Mohd Fadzil Faisae; Tiwari, Ashutosh; Hutabarat, Windo
    In a typical assembly optimisation, Assembly Sequence Planning and Assembly Line Balancing are performed independently. However, competition has compelled the manufacturer to innovate by integrating the optimisation of both problems. To incorporate ASP and ALB optimisations into a single integrated optimisation, a clear prerequisite is the availability of integrated ASP and ALB representation. Although many assembly representation works has been proposed, none of them fully meet the requirements of integrated optimisation because they were developed independently from various needs. In this paper, an integrated representation scheme for ASP and ALB that incorporate essential optimisation information is developed. The proposed representation scheme is built based on assembly tasks and represented using precedence graph and data matrix. The outcome from presented example showed that the information for ASP and ALB optimisation can be integrated and represented using task based precedence graph and data matrix, without discarding important attributes.
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    Multi-objective discrete particle swarm optimisation algorithm for integrated assembly sequence planning and assembly line balancing
    (Sage, 2016-10-24) Ab Rashid, Mohd Fadzil Faisae; Hutabarat, Windo; Tiwari, Ashutosh
    In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set.
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    A review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches
    (Springer Science Business Media, 2011-12-31T00:00:00Z) Ab Rashid, Mohd Fadzil Faisae; Hutabarat, Windo; Tiwari, Ashutosh
    Assembly optimisation activities occur across development and production stages of manufacturing goods. Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) problems are among the assembly optimisation. Both of these activities are classified as NP-hard. Several soft computing approaches using different techniques have been developed to solve ASP and ALB. Although these approaches do not guarantee the optimum solution, they have been successfully applied in many ASP and ALB optimisation works. This paper reported the survey on research in ASP and ALB that use soft computing approaches for the past 10years. To be more specific, only Simple Assembly Line Balancing Problem (SALBP) is considered for ALB. The survey shows that three soft computing algorithms that frequently used to solve ASP and ALB are Genetic Algorithm, Ant Colony Optimisation and Particle Swarm Optimisation. Meanwhile, the research in ASP and ALB is also progressing to the next level by integration of assembly optimisation activities across product development stages.