Operation sequencing using modified particle swarm optimization
Planning and scheduling (PS) problems in advanced manufacturing systems, such as flexible manufacturing systems, are composed of a set of interrelated problems, such as operation sequencing, machine selection. routing, and online scheduling. Operation sequencing deals with the problem of determining...
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| Format: | Conference or Workshop Item |
| Language: | en |
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2007
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| Online Access: | http://eprints.utm.my/10109/1/ZalmiyahZakaria2007_OperationSequencingUsingModifiedParticle.pdf http://eprints.utm.my/10109/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:102819 |
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| _version_ | 1845472308877066240 |
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| author | Zakaria, Zalmiyah Deris, Safaai |
| author_facet | Zakaria, Zalmiyah Deris, Safaai |
| author_sort | Zakaria, Zalmiyah |
| building | UTM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Malaysia |
| content_source | UTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Planning and scheduling (PS) problems in advanced manufacturing systems, such as flexible manufacturing systems, are composed of a set of interrelated problems, such as operation sequencing, machine selection. routing, and online scheduling. Operation sequencing deals with the problem of determining in what order to perform a set of selected operations such that the resulting sequence satisfies the precedence constraints as well as alternative operation constraints established by both the parts and operations. In this paper, modified particle swarm optimization (MPSO) has been used to generate a feasible operation sequence for a real world manufacturing problem. In addition, the directed mutation is used to accelerate the individuals move toward the optimal solutions. The quality of the result and its numerical performance is discussed in comparison with a standard genetic algorithm (SGA). After to runs, the result from SGA show that the possibilities for the solution to fall in the near optimal solution is about 30% compared with the result from MPSO which always force the constraints to befully satis.fied. |
| format | Conference or Workshop Item |
| id | my.utm.eprints-10109 |
| institution | Universiti Teknologi Malaysia |
| language | en |
| publishDate | 2007 |
| record_format | eprints |
| spelling | my.utm.eprints-101092020-02-29T13:43:30Z http://eprints.utm.my/10109/ Operation sequencing using modified particle swarm optimization Zakaria, Zalmiyah Deris, Safaai QA75 Electronic computers. Computer science Planning and scheduling (PS) problems in advanced manufacturing systems, such as flexible manufacturing systems, are composed of a set of interrelated problems, such as operation sequencing, machine selection. routing, and online scheduling. Operation sequencing deals with the problem of determining in what order to perform a set of selected operations such that the resulting sequence satisfies the precedence constraints as well as alternative operation constraints established by both the parts and operations. In this paper, modified particle swarm optimization (MPSO) has been used to generate a feasible operation sequence for a real world manufacturing problem. In addition, the directed mutation is used to accelerate the individuals move toward the optimal solutions. The quality of the result and its numerical performance is discussed in comparison with a standard genetic algorithm (SGA). After to runs, the result from SGA show that the possibilities for the solution to fall in the near optimal solution is about 30% compared with the result from MPSO which always force the constraints to befully satis.fied. 2007 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/10109/1/ZalmiyahZakaria2007_OperationSequencingUsingModifiedParticle.pdf Zakaria, Zalmiyah and Deris, Safaai (2007) Operation sequencing using modified particle swarm optimization. In: Fifth International Conference on Information Technology in Asia 2007, 9-12th July 2007, Kuching, Sarawak, Malaysia. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:102819 |
| spellingShingle | QA75 Electronic computers. Computer science Zakaria, Zalmiyah Deris, Safaai Operation sequencing using modified particle swarm optimization |
| title | Operation sequencing using modified particle swarm optimization |
| title_full | Operation sequencing using modified particle swarm optimization |
| title_fullStr | Operation sequencing using modified particle swarm optimization |
| title_full_unstemmed | Operation sequencing using modified particle swarm optimization |
| title_short | Operation sequencing using modified particle swarm optimization |
| title_sort | operation sequencing using modified particle swarm optimization |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/10109/1/ZalmiyahZakaria2007_OperationSequencingUsingModifiedParticle.pdf http://eprints.utm.my/10109/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:102819 |
| url_provider | http://eprints.utm.my/ |