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| Title | A revised discrete particle swarm optimization algorithm for permutation flow-shop scheduling problem |
| Creator | 陳春龍 Chen, Chun-Lung;Huang, Shin-Ying;Tzeng, Yeu-Ruey;Chen, Chuen-Lung |
| Contributor | 資管系 |
| Key Words | Permutation flow-shop scheduling problem ;Particle swarm optimization ; Makespan |
| Date | 2013-12 |
| Date Issued | 12-Feb-2015 14:19:38 (UTC+8) |
| Summary | This research proposes a revised discrete particle swarm optimization (RDPSO) to solve the permutation flow-shop scheduling problem with the objective of minimizing makespan (PFSP-makespan). The candidate problem is one of the most studied NP-complete scheduling problems. RDPSO proposes new particle swarm learning strategies to thoroughly study how to properly apply the global best solution and the personal best solution to guide the search of RDPSO. A new filtered local search is developed to filter the solution regions that have been reviewed and guide the search to new solution regions in order to keep the search from premature convergence. Computational experiments on Taillard’s benchmark problem sets demonstrate that RDPSO significantly outperforms all the existing PSO algorithms. |
| Relation | Soft Computing,18(11),2271-2282 |
| Type | article |
| DOI | http://dx.doi.org/10.1007/s00500-013-1199-z |
| dc.contributor | 資管系 | - |
| dc.creator (作者) | 陳春龍 | - |
| dc.creator (作者) | Chen, Chun-Lung;Huang, Shin-Ying;Tzeng, Yeu-Ruey;Chen, Chuen-Lung | - |
| dc.date (日期) | 2013-12 | - |
| dc.date.accessioned | 12-Feb-2015 14:19:38 (UTC+8) | - |
| dc.date.available | 12-Feb-2015 14:19:38 (UTC+8) | - |
| dc.date.issued (上傳時間) | 12-Feb-2015 14:19:38 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/73498 | - |
| dc.description.abstract (摘要) | This research proposes a revised discrete particle swarm optimization (RDPSO) to solve the permutation flow-shop scheduling problem with the objective of minimizing makespan (PFSP-makespan). The candidate problem is one of the most studied NP-complete scheduling problems. RDPSO proposes new particle swarm learning strategies to thoroughly study how to properly apply the global best solution and the personal best solution to guide the search of RDPSO. A new filtered local search is developed to filter the solution regions that have been reviewed and guide the search to new solution regions in order to keep the search from premature convergence. Computational experiments on Taillard’s benchmark problem sets demonstrate that RDPSO significantly outperforms all the existing PSO algorithms. | - |
| dc.format.extent | 467467 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Soft Computing,18(11),2271-2282 | - |
| dc.subject (關鍵詞) | Permutation flow-shop scheduling problem ;Particle swarm optimization ; Makespan | - |
| dc.title (題名) | A revised discrete particle swarm optimization algorithm for permutation flow-shop scheduling problem | - |
| dc.type (資料類型) | article | en |
| dc.identifier.doi (DOI) | 10.1007/s00500-013-1199-z | en_US |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1007/s00500-013-1199-z | en_US |