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Assessment of integrated assembly sequence planning and line balancing optimization using metaheuristic algorithms

In assembly optimization, there has been an integration of Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) optimization, taking into account the advantages of improved solution quality, reduced error rates, and faster time-to-market for products. Previously, only a limited number...

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Main Authors: Mohd Fadzil Faisae, Ab Rashid, Ullah, Wasif, Muhammad Ammar, Nik Mu’tasim
Format: Conference or Workshop Item
Language:English
English
Published: IEEE 2024
Subjects:
TJ Mechanical engineering and machinery
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/42703/1/Assessment_of_Integrated_Assembly_Sequence_Planning_and_Line_Balancing_Optimization_Using_Metaheuristic_Algorithms.pdf
http://umpir.ump.edu.my/id/eprint/42703/7/Assessment%20of%20integrated%20assembly%20sequence%20planning_ABST.pdf
http://umpir.ump.edu.my/id/eprint/42703/
https://doi.org/10.1109/ISCI62787.2024.10668162
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spelling my.ump.umpir.427032024-10-02T04:15:05Z http://umpir.ump.edu.my/id/eprint/42703/ Assessment of integrated assembly sequence planning and line balancing optimization using metaheuristic algorithms Mohd Fadzil Faisae, Ab Rashid Ullah, Wasif Muhammad Ammar, Nik Mu’tasim TJ Mechanical engineering and machinery TS Manufactures In assembly optimization, there has been an integration of Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) optimization, taking into account the advantages of improved solution quality, reduced error rates, and faster time-to-market for products. Previously, only a limited number of publications explored the integrated ASP and ALB optimization. These studies primarily compared the performance of algorithms within the Genetic Algorithm and Ant Colony Optimization classes. Moreover, the number of test problems used in these works was restricted to only three problems. In an ideal scenario, the efficacy of an algorithm can only be deduced when it is tested across a wide range of problem types. In this paper, the performance of six different metaheuristic algorithms for optimizing integrated ASP and ALB are compared. These algorithms include Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). To rigorously test these metaheuristic algorithms, 45 test problems of various sizes were employed to evaluate their performance across different categories. The results show that ACO outperforms in larger sized problems, while PSO exhibits potential to be explored further due to its satisfactory overall performance in terms of solution quality and distribution. IEEE 2024 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42703/1/Assessment_of_Integrated_Assembly_Sequence_Planning_and_Line_Balancing_Optimization_Using_Metaheuristic_Algorithms.pdf pdf en http://umpir.ump.edu.my/id/eprint/42703/7/Assessment%20of%20integrated%20assembly%20sequence%20planning_ABST.pdf Mohd Fadzil Faisae, Ab Rashid and Ullah, Wasif and Muhammad Ammar, Nik Mu’tasim (2024) Assessment of integrated assembly sequence planning and line balancing optimization using metaheuristic algorithms. In: 2024 IEEE 6th Symposium on Computers & Informatics (ISCI). 2024 IEEE 6th Symposium on Computers & Informatics (ISCI) , 10 August 2024 , Kuala Lumpur, Malaysia. pp. 55-59.. ISSN 2996-6752 ISBN 979-8-3503-5385-3 (Published) https://doi.org/10.1109/ISCI62787.2024.10668162
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic TJ Mechanical engineering and machinery
TS Manufactures
spellingShingle TJ Mechanical engineering and machinery
TS Manufactures
Mohd Fadzil Faisae, Ab Rashid
Ullah, Wasif
Muhammad Ammar, Nik Mu’tasim
Assessment of integrated assembly sequence planning and line balancing optimization using metaheuristic algorithms
description In assembly optimization, there has been an integration of Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) optimization, taking into account the advantages of improved solution quality, reduced error rates, and faster time-to-market for products. Previously, only a limited number of publications explored the integrated ASP and ALB optimization. These studies primarily compared the performance of algorithms within the Genetic Algorithm and Ant Colony Optimization classes. Moreover, the number of test problems used in these works was restricted to only three problems. In an ideal scenario, the efficacy of an algorithm can only be deduced when it is tested across a wide range of problem types. In this paper, the performance of six different metaheuristic algorithms for optimizing integrated ASP and ALB are compared. These algorithms include Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). To rigorously test these metaheuristic algorithms, 45 test problems of various sizes were employed to evaluate their performance across different categories. The results show that ACO outperforms in larger sized problems, while PSO exhibits potential to be explored further due to its satisfactory overall performance in terms of solution quality and distribution.
format Conference or Workshop Item
author Mohd Fadzil Faisae, Ab Rashid
Ullah, Wasif
Muhammad Ammar, Nik Mu’tasim
author_facet Mohd Fadzil Faisae, Ab Rashid
Ullah, Wasif
Muhammad Ammar, Nik Mu’tasim
author_sort Mohd Fadzil Faisae, Ab Rashid
title Assessment of integrated assembly sequence planning and line balancing optimization using metaheuristic algorithms
title_short Assessment of integrated assembly sequence planning and line balancing optimization using metaheuristic algorithms
title_full Assessment of integrated assembly sequence planning and line balancing optimization using metaheuristic algorithms
title_fullStr Assessment of integrated assembly sequence planning and line balancing optimization using metaheuristic algorithms
title_full_unstemmed Assessment of integrated assembly sequence planning and line balancing optimization using metaheuristic algorithms
title_sort assessment of integrated assembly sequence planning and line balancing optimization using metaheuristic algorithms
publisher IEEE
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/42703/1/Assessment_of_Integrated_Assembly_Sequence_Planning_and_Line_Balancing_Optimization_Using_Metaheuristic_Algorithms.pdf
http://umpir.ump.edu.my/id/eprint/42703/7/Assessment%20of%20integrated%20assembly%20sequence%20planning_ABST.pdf
http://umpir.ump.edu.my/id/eprint/42703/
https://doi.org/10.1109/ISCI62787.2024.10668162
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score 13.235362

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