Integrated optimization of mixed-model assembly sequence planning and line balancing using multi-objective discrete particle swarm optimization
Recently, interest in integrated assembly sequence planning (ASP) and assembly line balancing (ALB) began to pick up because of its numerous benefits, such as the larger search space that leads to better solution quality, reduced error rate in planning, and expedited product time-to-market. However,...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2019
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| Online Access: | http://umpir.ump.edu.my/id/eprint/25651/1/2019%20MM-ASPALB%20AIEDAM.pdf |
| _version_ | 1796993505905606656 |
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| author | M. F. F., Ab Rashid Tiwari, Ashutosh Hutabarat, Windo |
| author_facet | M. F. F., Ab Rashid Tiwari, Ashutosh Hutabarat, Windo |
| author_sort | M. F. F., Ab Rashid |
| collection | UMP |
| description | Recently, interest in integrated assembly sequence planning (ASP) and assembly line balancing (ALB) began to pick up because of its numerous benefits, such as the larger search space that leads to better solution quality, reduced error rate in planning, and expedited product time-to-market. However, existing research is limited to the simple assembly problem that only runs one homogenous product. This paper therefore models and optimizes the integrated mixed-model ASP and ALB using Multi-objective Discrete Particle Swarm Optimization (MODPSO) concurrently. This is a new variant of the integrated assembly problem. The integrated mixed-model ASP and ALB is modeled using task-based joint precedence graph. In order to test the performance of MODPSO to optimize the integrated mixed-model ASP and ALB, an experiment using a set of 51 test problems with different difficulty levels was conducted. Besides that, MODPSO coefficient tuning was also conducted to identify the best setting so as to optimize the problem. The results from this experiment indicated that the MODPSO algorithm presents a significant improvement in term of solution quality toward Pareto optimal and demonstrates the ability to explore the extreme solutions in the mixed-model assembly optimization search space. The originality of this research is on the new variant of integrated ASP and ALB problem. This paper is the first published research to model and optimize the integrated ASP and ALB research for mixed-model assembly problem. |
| first_indexed | 2024-03-06T12:34:59Z |
| format | Article |
| id | UMPir25651 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:34:59Z |
| publishDate | 2019 |
| publisher | Cambridge University Press |
| record_format | dspace |
| spelling | UMPir256512019-11-21T02:56:23Z http://umpir.ump.edu.my/id/eprint/25651/ Integrated optimization of mixed-model assembly sequence planning and line balancing using multi-objective discrete particle swarm optimization M. F. F., Ab Rashid Tiwari, Ashutosh Hutabarat, Windo TS Manufactures Recently, interest in integrated assembly sequence planning (ASP) and assembly line balancing (ALB) began to pick up because of its numerous benefits, such as the larger search space that leads to better solution quality, reduced error rate in planning, and expedited product time-to-market. However, existing research is limited to the simple assembly problem that only runs one homogenous product. This paper therefore models and optimizes the integrated mixed-model ASP and ALB using Multi-objective Discrete Particle Swarm Optimization (MODPSO) concurrently. This is a new variant of the integrated assembly problem. The integrated mixed-model ASP and ALB is modeled using task-based joint precedence graph. In order to test the performance of MODPSO to optimize the integrated mixed-model ASP and ALB, an experiment using a set of 51 test problems with different difficulty levels was conducted. Besides that, MODPSO coefficient tuning was also conducted to identify the best setting so as to optimize the problem. The results from this experiment indicated that the MODPSO algorithm presents a significant improvement in term of solution quality toward Pareto optimal and demonstrates the ability to explore the extreme solutions in the mixed-model assembly optimization search space. The originality of this research is on the new variant of integrated ASP and ALB problem. This paper is the first published research to model and optimize the integrated ASP and ALB research for mixed-model assembly problem. Cambridge University Press 2019-08-01 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25651/1/2019%20MM-ASPALB%20AIEDAM.pdf M. F. F., Ab Rashid and Tiwari, Ashutosh and Hutabarat, Windo (2019) Integrated optimization of mixed-model assembly sequence planning and line balancing using multi-objective discrete particle swarm optimization. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 33 (3). pp. 332-345. ISSN 1469-1760. (Published) https://doi.org/10.1017/S0890060419000131 |
| spellingShingle | TS Manufactures M. F. F., Ab Rashid Tiwari, Ashutosh Hutabarat, Windo Integrated optimization of mixed-model assembly sequence planning and line balancing using multi-objective discrete particle swarm optimization |
| title | Integrated optimization of mixed-model assembly sequence planning and line balancing using multi-objective discrete particle swarm optimization |
| title_full | Integrated optimization of mixed-model assembly sequence planning and line balancing using multi-objective discrete particle swarm optimization |
| title_fullStr | Integrated optimization of mixed-model assembly sequence planning and line balancing using multi-objective discrete particle swarm optimization |
| title_full_unstemmed | Integrated optimization of mixed-model assembly sequence planning and line balancing using multi-objective discrete particle swarm optimization |
| title_short | Integrated optimization of mixed-model assembly sequence planning and line balancing using multi-objective discrete particle swarm optimization |
| title_sort | integrated optimization of mixed model assembly sequence planning and line balancing using multi objective discrete particle swarm optimization |
| topic | TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/25651/1/2019%20MM-ASPALB%20AIEDAM.pdf |
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