Heuristic driven genetic algorithm for job-shop scheduling
In this project, a local search algorithm has been implemented on a job-shop scheduling program[l] given at the initial stage of the project. The program was tested on 35 JSS benchmarks. The results show a range of performance that is 10% to 15% from the known optimum. Besides proposing and implemen...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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2011
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| Online Access: | http://hdl.handle.net/10356/42789 |
| _version_ | 1824456436940275712 |
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| author | Chang, Sau Leng. |
| author2 | Lim Meng Hiot |
| author_facet | Lim Meng Hiot Chang, Sau Leng. |
| author_sort | Chang, Sau Leng. |
| collection | NTU |
| description | In this project, a local search algorithm has been implemented on a job-shop scheduling program[l] given at the initial stage of the project. The program was tested on 35 JSS benchmarks. The results show a range of performance that is 10% to 15% from the known optimum. Besides proposing and implementing the local search algorithm, the original JSS program is also ported to run on Win32 platforms(Win95 and NT). Its memory handling functions are modified to dynamically handle job-shop problems of any sizes from 5x5 up to 30x30. A Windows based graphical user interface(GUI) is written so that the user is able to modify benchmark specific parameters on the GUI and run several benchmarks in batch mode. |
| first_indexed | 2025-02-19T03:54:05Z |
| format | Thesis |
| id | ntu-10356/42789 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:54:05Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | ntu-10356/427892023-07-04T15:02:29Z Heuristic driven genetic algorithm for job-shop scheduling Chang, Sau Leng. Lim Meng Hiot School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering In this project, a local search algorithm has been implemented on a job-shop scheduling program[l] given at the initial stage of the project. The program was tested on 35 JSS benchmarks. The results show a range of performance that is 10% to 15% from the known optimum. Besides proposing and implementing the local search algorithm, the original JSS program is also ported to run on Win32 platforms(Win95 and NT). Its memory handling functions are modified to dynamically handle job-shop problems of any sizes from 5x5 up to 30x30. A Windows based graphical user interface(GUI) is written so that the user is able to modify benchmark specific parameters on the GUI and run several benchmarks in batch mode. Master of Science (Consumer Electronics) 2011-01-11T05:42:58Z 2011-01-11T05:42:58Z 2000 2000 Thesis http://hdl.handle.net/10356/42789 en 55 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Chang, Sau Leng. Heuristic driven genetic algorithm for job-shop scheduling |
| title | Heuristic driven genetic algorithm for job-shop scheduling |
| title_full | Heuristic driven genetic algorithm for job-shop scheduling |
| title_fullStr | Heuristic driven genetic algorithm for job-shop scheduling |
| title_full_unstemmed | Heuristic driven genetic algorithm for job-shop scheduling |
| title_short | Heuristic driven genetic algorithm for job-shop scheduling |
| title_sort | heuristic driven genetic algorithm for job shop scheduling |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/42789 |
| work_keys_str_mv | AT changsauleng heuristicdrivengeneticalgorithmforjobshopscheduling |