Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources
This paper solved the unrelated parallel machine scheduling with additional resources (UPMR) problem. The processing time and the number of required resources for each job rely on the machine that does the processing. Each job j needed units of resources (rjm) during its time of processing on a mach...
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| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science
2022
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/34633/1/Guided%20genetic%20algorithm%20for%20solving%20unrelated%20parallel%20machine%20scheduling.pdf |
| _version_ | 1796995187854016512 |
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| author | Abed, Munther Hameed Mohd Nizam Mohmad, Kahar |
| author_facet | Abed, Munther Hameed Mohd Nizam Mohmad, Kahar |
| author_sort | Abed, Munther Hameed |
| collection | UMP |
| description | This paper solved the unrelated parallel machine scheduling with additional resources (UPMR) problem. The processing time and the number of required resources for each job rely on the machine that does the processing. Each job j needed units of resources (rjm) during its time of processing on a machine m. These additional resources are limited, and this made the UPMR a difficult problem to solve. In this study, the maximum completion time of jobs makespan must be minimized. Here, we proposed genetic algorithm (GA) to solve the UPMR problem because of the robustness and the success of GA in solving many optimization problems. An enhancement of GA was also proposed in this work. Generally, the experiment involves tuning the parameters of GA. Additionally, an appropriate selection of GA operators was also experimented. The guide genetic algorithm (GGA) is not used to solve the unspecified dynamic UPMR. Besides, the utilization of parameters tuning and operators gave a balance between exploration and exploitation and thus help the search escape the local optimum. Results show that the GGA outperforms the simple genetic algorithm (SGA), but it still didn't match the results in the literature. On the other hand, GGA significantly outperforms all methods in terms of CPU time. |
| first_indexed | 2024-03-06T12:58:37Z |
| format | Article |
| id | UMPir34633 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:58:37Z |
| publishDate | 2022 |
| publisher | Institute of Advanced Engineering and Science |
| record_format | dspace |
| spelling | UMPir346332023-03-14T06:46:42Z http://umpir.ump.edu.my/id/eprint/34633/ Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources Abed, Munther Hameed Mohd Nizam Mohmad, Kahar QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) This paper solved the unrelated parallel machine scheduling with additional resources (UPMR) problem. The processing time and the number of required resources for each job rely on the machine that does the processing. Each job j needed units of resources (rjm) during its time of processing on a machine m. These additional resources are limited, and this made the UPMR a difficult problem to solve. In this study, the maximum completion time of jobs makespan must be minimized. Here, we proposed genetic algorithm (GA) to solve the UPMR problem because of the robustness and the success of GA in solving many optimization problems. An enhancement of GA was also proposed in this work. Generally, the experiment involves tuning the parameters of GA. Additionally, an appropriate selection of GA operators was also experimented. The guide genetic algorithm (GGA) is not used to solve the unspecified dynamic UPMR. Besides, the utilization of parameters tuning and operators gave a balance between exploration and exploitation and thus help the search escape the local optimum. Results show that the GGA outperforms the simple genetic algorithm (SGA), but it still didn't match the results in the literature. On the other hand, GGA significantly outperforms all methods in terms of CPU time. Institute of Advanced Engineering and Science 2022-05 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/34633/1/Guided%20genetic%20algorithm%20for%20solving%20unrelated%20parallel%20machine%20scheduling.pdf Abed, Munther Hameed and Mohd Nizam Mohmad, Kahar (2022) Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources. Indonesian Journal of Electrical Engineering and Computer Science, 26 (2). pp. 1036-1049. ISSN 2502-4752. (Published) https://doi.org/10.11591/ijeecs.v26.i2.pp1036-1049 https://doi.org/10.11591/ijeecs.v26.i2.pp1036-1049 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Abed, Munther Hameed Mohd Nizam Mohmad, Kahar Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources |
| title | Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources |
| title_full | Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources |
| title_fullStr | Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources |
| title_full_unstemmed | Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources |
| title_short | Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources |
| title_sort | guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/34633/1/Guided%20genetic%20algorithm%20for%20solving%20unrelated%20parallel%20machine%20scheduling.pdf |
| work_keys_str_mv | AT abedmuntherhameed guidedgeneticalgorithmforsolvingunrelatedparallelmachineschedulingproblemwithadditionalresources AT mohdnizammohmadkahar guidedgeneticalgorithmforsolvingunrelatedparallelmachineschedulingproblemwithadditionalresources |