Learnable evolutionary model for flexible job-shop scheduling
196 p.
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| Other Authors: | |
| Format: | Thesis |
| Published: |
2010
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| Online Access: | https://hdl.handle.net/10356/35728 |
| _version_ | 1811689806360477696 |
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| author | Ho Nhu Binh |
| author2 | Tay Joc Cing |
| author_facet | Tay Joc Cing Ho Nhu Binh |
| author_sort | Ho Nhu Binh |
| collection | NTU |
| description | 196 p. |
| first_indexed | 2024-10-01T05:53:57Z |
| format | Thesis |
| id | ntu-10356/35728 |
| institution | Nanyang Technological University |
| last_indexed | 2024-10-01T05:53:57Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-10356/357282023-03-04T00:46:42Z Learnable evolutionary model for flexible job-shop scheduling Ho Nhu Binh Tay Joc Cing School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence DRNTU::Engineering::Manufacturing::Production management 196 p. In recent years, resource allocation problems have received much attention in the research community and industry. The most frequently encountered problems of these topics are the Job-Shop Scheduling Problem (JSP). Effective scheduling for the JSP has the potential to decrease cost and increase profits. The JSP can be modeled as the allocation of machines over specific time to process a collection of jobs. An extension of the JSP which makes this problem more difficult is the Flexible Job-Shop Scheduling Problem (FJSP). Unfortunately, the traditional deterministic approaches, such as Branch and Bound, Priority Dispatching Rules, have scaled poorly with large problem size. The current local search techniques have also performed in expensive computational time. Currently, the interaction between evolution and learning has received much attention from the research community. Some recent studies on machine learning have shown that it can significantly improve the efficiency of problem solving when using Evolutionary Algorithms (EAs). DOCTOR OF PHILOSOPHY (SCE) 2010-04-23T01:31:09Z 2010-04-23T01:31:09Z 2007 2007 Thesis Ho, N. B. (2007). Learnable evolutionary model for flexible job-shop scheduling. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/35728 10.32657/10356/35728 application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence DRNTU::Engineering::Manufacturing::Production management Ho Nhu Binh Learnable evolutionary model for flexible job-shop scheduling |
| title | Learnable evolutionary model for flexible job-shop scheduling |
| title_full | Learnable evolutionary model for flexible job-shop scheduling |
| title_fullStr | Learnable evolutionary model for flexible job-shop scheduling |
| title_full_unstemmed | Learnable evolutionary model for flexible job-shop scheduling |
| title_short | Learnable evolutionary model for flexible job-shop scheduling |
| title_sort | learnable evolutionary model for flexible job shop scheduling |
| topic | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence DRNTU::Engineering::Manufacturing::Production management |
| url | https://hdl.handle.net/10356/35728 |
| work_keys_str_mv | AT honhubinh learnableevolutionarymodelforflexiblejobshopscheduling |