Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints
Mixed-model assembly line balancing problem (MMALBP) is an NP-hard problem whichrequires an effective algorithm for solution. In this study, an assessment of metaheuristic algorithms to optimize MMALBP was conductedby using four popular metaheuristics , namely particle swarm optimiza...
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/30043/ http://umpir.ump.edu.my/id/eprint/30043/1/Assessment%20of%20metaheuristic%20algorithms%20to%20optimize%20of%20mixed.pdf |
| _version_ | 1848823420981084160 |
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| author | M.M., Razali M. F. F., Ab Rashid M. R. A., Make |
| author_facet | M.M., Razali M. F. F., Ab Rashid M. R. A., Make |
| author_sort | M.M., Razali |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Mixed-model assembly line balancing problem (MMALBP) is an NP-hard problem whichrequires an effective algorithm for solution. In this study, an assessment of metaheuristic algorithms to optimize MMALBP was conductedby using four popular metaheuristics , namely particle swarm optimization (PSO), simulated annealing (SA), ant colony optimization (ACO),and genetic algorithm (GA). Three categories of test problem (small, medium, and large) wereused,ranging from 8 to 100 tasks.For computational experiment, MATLAB software wasused toinvestigate the metaheuristic algorithmperformances to optimize the designated objective functions. Results revealedthat the ACO algorithm performed better in termsof finding the best fitness functions when dealing with many tasks.Averagely, it producedbetter solution qualitythan PSO by 5.82%, GA by 9.80%, and SA by 7.66%. However, PSO was more superior in termsof processing time as compared to ACO by 29.25%, GA by 40.54%, and SA by 73.23%.Therefore, future research directions,such as by using the actual manufacturing assembly line data to test the algorithm performances,are likely to happen. |
| first_indexed | 2025-11-15T02:56:52Z |
| format | Article |
| id | ump-30043 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:56:52Z |
| publishDate | 2020 |
| publisher | Penerbit UMP |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-300432021-03-03T05:44:10Z http://umpir.ump.edu.my/id/eprint/30043/ Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints M.M., Razali M. F. F., Ab Rashid M. R. A., Make TS Manufactures Mixed-model assembly line balancing problem (MMALBP) is an NP-hard problem whichrequires an effective algorithm for solution. In this study, an assessment of metaheuristic algorithms to optimize MMALBP was conductedby using four popular metaheuristics , namely particle swarm optimization (PSO), simulated annealing (SA), ant colony optimization (ACO),and genetic algorithm (GA). Three categories of test problem (small, medium, and large) wereused,ranging from 8 to 100 tasks.For computational experiment, MATLAB software wasused toinvestigate the metaheuristic algorithmperformances to optimize the designated objective functions. Results revealedthat the ACO algorithm performed better in termsof finding the best fitness functions when dealing with many tasks.Averagely, it producedbetter solution qualitythan PSO by 5.82%, GA by 9.80%, and SA by 7.66%. However, PSO was more superior in termsof processing time as compared to ACO by 29.25%, GA by 40.54%, and SA by 73.23%.Therefore, future research directions,such as by using the actual manufacturing assembly line data to test the algorithm performances,are likely to happen. Penerbit UMP 2020 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/30043/1/Assessment%20of%20metaheuristic%20algorithms%20to%20optimize%20of%20mixed.pdf M.M., Razali and M. F. F., Ab Rashid and M. R. A., Make (2020) Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints. Journal of Modern Manufacturing Systems and Technology (JMMST), 4 (2). pp. 73-83. ISSN 2636-9575. (Published) https://doi.org/10.15282/jmmst.v4i2.4787 https://doi.org/10.15282/jmmst.v4i2.4787 |
| spellingShingle | TS Manufactures M.M., Razali M. F. F., Ab Rashid M. R. A., Make Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints |
| title | Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints |
| title_full | Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints |
| title_fullStr | Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints |
| title_full_unstemmed | Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints |
| title_short | Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints |
| title_sort | assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints |
| topic | TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/30043/ http://umpir.ump.edu.my/id/eprint/30043/ http://umpir.ump.edu.my/id/eprint/30043/ http://umpir.ump.edu.my/id/eprint/30043/1/Assessment%20of%20metaheuristic%20algorithms%20to%20optimize%20of%20mixed.pdf |