A Bee Colony Algorithm based Solver for Flow Shop Scheduling Problem
Flow Shop Scheduling (FSS) is scheduled to involve n jobs and m machines in the same process sequence, where each machine processes precisely one job in a certain period. In FSS, when a machine is doing work, other machines cannot do the same job simultaneously. The solution to this problem is the j...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Padang
2021-05-01
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| Series: | JOIV: International Journal on Informatics Visualization |
| Subjects: | |
| Online Access: | https://joiv.org/index.php/joiv/article/view/491 |
| _version_ | 1827998714238599168 |
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| author | Yosua Halim Cecilia Esti Nugraheni |
| author_facet | Yosua Halim Cecilia Esti Nugraheni |
| author_sort | Yosua Halim |
| collection | DOAJ |
| description | Flow Shop Scheduling (FSS) is scheduled to involve n jobs and m machines in the same process sequence, where each machine processes precisely one job in a certain period. In FSS, when a machine is doing work, other machines cannot do the same job simultaneously. The solution to this problem is the job sequence with minimal total processing time. Many algorithms can be used to determine the order in which the job is performed. In this paper, the algorithm used to solve the flow shop scheduling problem is the bee colony algorithm. The bee colony algorithm is an algorithm that applies the metaheuristic method and performs optimization according to the workings of the bee colony. To measure the performance of this algorithm, we conducted some experiments by using Taillard's Benchmark as problem instances. Based on experiments that have been carried out by changing the existing parameter values, the size of the bee population, the number of iterations, and the limit number of bees can affect the candidate solutions obtained. The limit is a control parameter for a bee when looking for new food sources. The more the number of bees, the more iterations, and the limit used, the better the final time of the sequence of work. The bee colony algorithm can reach the upper limit of the Taillard case in some cases in the number of machines 5 and 20 jobs. The more the number of machines and jobs to optimize, the worse the total processing time. |
| first_indexed | 2024-04-10T05:48:08Z |
| format | Article |
| id | doaj.art-fc29fff440b94e6a99616e5c5ac82a9b |
| institution | Directory Open Access Journal |
| issn | 2549-9610 2549-9904 |
| language | English |
| last_indexed | 2024-04-10T05:48:08Z |
| publishDate | 2021-05-01 |
| publisher | Politeknik Negeri Padang |
| record_format | Article |
| series | JOIV: International Journal on Informatics Visualization |
| spelling | doaj.art-fc29fff440b94e6a99616e5c5ac82a9b2023-03-05T10:30:14ZengPoliteknik Negeri PadangJOIV: International Journal on Informatics Visualization2549-96102549-99042021-05-015217017610.30630/joiv.5.2.491264A Bee Colony Algorithm based Solver for Flow Shop Scheduling ProblemYosua Halim0Cecilia Esti Nugraheni1Department of Informatics, Parahyangan Catholic University, Jl. Ciumbueuit 94, Bandung,40141, IndonesiaDepartment of Informatics, Parahyangan Catholic University, Jl. Ciumbueuit 94, Bandung,40141, IndonesiaFlow Shop Scheduling (FSS) is scheduled to involve n jobs and m machines in the same process sequence, where each machine processes precisely one job in a certain period. In FSS, when a machine is doing work, other machines cannot do the same job simultaneously. The solution to this problem is the job sequence with minimal total processing time. Many algorithms can be used to determine the order in which the job is performed. In this paper, the algorithm used to solve the flow shop scheduling problem is the bee colony algorithm. The bee colony algorithm is an algorithm that applies the metaheuristic method and performs optimization according to the workings of the bee colony. To measure the performance of this algorithm, we conducted some experiments by using Taillard's Benchmark as problem instances. Based on experiments that have been carried out by changing the existing parameter values, the size of the bee population, the number of iterations, and the limit number of bees can affect the candidate solutions obtained. The limit is a control parameter for a bee when looking for new food sources. The more the number of bees, the more iterations, and the limit used, the better the final time of the sequence of work. The bee colony algorithm can reach the upper limit of the Taillard case in some cases in the number of machines 5 and 20 jobs. The more the number of machines and jobs to optimize, the worse the total processing time.https://joiv.org/index.php/joiv/article/view/491schedulingflow shop schedulingmetaheuristicsbee colony algorithm. |
| spellingShingle | Yosua Halim Cecilia Esti Nugraheni A Bee Colony Algorithm based Solver for Flow Shop Scheduling Problem JOIV: International Journal on Informatics Visualization scheduling flow shop scheduling metaheuristics bee colony algorithm. |
| title | A Bee Colony Algorithm based Solver for Flow Shop Scheduling Problem |
| title_full | A Bee Colony Algorithm based Solver for Flow Shop Scheduling Problem |
| title_fullStr | A Bee Colony Algorithm based Solver for Flow Shop Scheduling Problem |
| title_full_unstemmed | A Bee Colony Algorithm based Solver for Flow Shop Scheduling Problem |
| title_short | A Bee Colony Algorithm based Solver for Flow Shop Scheduling Problem |
| title_sort | bee colony algorithm based solver for flow shop scheduling problem |
| topic | scheduling flow shop scheduling metaheuristics bee colony algorithm. |
| url | https://joiv.org/index.php/joiv/article/view/491 |
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