Modified genetic algorithm to solve worker assignment problem with time windows
Abstract In recent years, the demand for electronic products has been increasing rapidly. T mounting technology (SMT) line is one of the production areas for electronic products, directly affecting this situation. In an SMT line, multiple machines mount electronic parts to the board. The worker must...
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| Format: | Article |
| Language: | English |
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Springer
2024-02-01
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| Series: | Industrial Artificial Intelligence |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44244-024-00015-9 |
| _version_ | 1827328401490313216 |
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| author | Alfian Akbar Gozali |
| author_facet | Alfian Akbar Gozali |
| author_sort | Alfian Akbar Gozali |
| collection | DOAJ |
| description | Abstract In recent years, the demand for electronic products has been increasing rapidly. T mounting technology (SMT) line is one of the production areas for electronic products, directly affecting this situation. In an SMT line, multiple machines mount electronic parts to the board. The worker must complete work when the parts used in these machines are within the remaining parts available for replacement. When a worker fails to replace parts at the right time, the production line stops, and delays occur. Besides, there may be a designated worker who should be assigned to each task. In the current situation, workers’ work procedures are not optimized, so they should schedule work procedures for each worker. This problem is called Worker Assignment Problem with Time Window (WAPTW). This paper proposes a method to solve WAPTW called Genetic Algorithm with Local Restriction (GALR). GALR combines a genetic algorithm (GA) and local search with local restriction. This paper’s main contribution is introducing WAPTW as a novel real-world optimization problem in an electricity company, its mathematical formulation, and a proposed GALR to solve WAPTW. The experiment shows that the proposed method could yield the best result in real-world WAPTW compared with other methods. |
| first_indexed | 2024-03-07T15:17:24Z |
| format | Article |
| id | doaj.art-cd7163cd463a48e086d8d1ddf97ac5ea |
| institution | Directory Open Access Journal |
| issn | 2731-667X |
| language | English |
| last_indexed | 2024-03-07T15:17:24Z |
| publishDate | 2024-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Industrial Artificial Intelligence |
| spelling | doaj.art-cd7163cd463a48e086d8d1ddf97ac5ea2024-03-05T17:51:54ZengSpringerIndustrial Artificial Intelligence2731-667X2024-02-012111010.1007/s44244-024-00015-9Modified genetic algorithm to solve worker assignment problem with time windowsAlfian Akbar Gozali0Faculty of Applied Science, Telkom UniversityAbstract In recent years, the demand for electronic products has been increasing rapidly. T mounting technology (SMT) line is one of the production areas for electronic products, directly affecting this situation. In an SMT line, multiple machines mount electronic parts to the board. The worker must complete work when the parts used in these machines are within the remaining parts available for replacement. When a worker fails to replace parts at the right time, the production line stops, and delays occur. Besides, there may be a designated worker who should be assigned to each task. In the current situation, workers’ work procedures are not optimized, so they should schedule work procedures for each worker. This problem is called Worker Assignment Problem with Time Window (WAPTW). This paper proposes a method to solve WAPTW called Genetic Algorithm with Local Restriction (GALR). GALR combines a genetic algorithm (GA) and local search with local restriction. This paper’s main contribution is introducing WAPTW as a novel real-world optimization problem in an electricity company, its mathematical formulation, and a proposed GALR to solve WAPTW. The experiment shows that the proposed method could yield the best result in real-world WAPTW compared with other methods.https://doi.org/10.1007/s44244-024-00015-9Worker assignmentGenetic algorithmLocal searchLocal restriction |
| spellingShingle | Alfian Akbar Gozali Modified genetic algorithm to solve worker assignment problem with time windows Industrial Artificial Intelligence Worker assignment Genetic algorithm Local search Local restriction |
| title | Modified genetic algorithm to solve worker assignment problem with time windows |
| title_full | Modified genetic algorithm to solve worker assignment problem with time windows |
| title_fullStr | Modified genetic algorithm to solve worker assignment problem with time windows |
| title_full_unstemmed | Modified genetic algorithm to solve worker assignment problem with time windows |
| title_short | Modified genetic algorithm to solve worker assignment problem with time windows |
| title_sort | modified genetic algorithm to solve worker assignment problem with time windows |
| topic | Worker assignment Genetic algorithm Local search Local restriction |
| url | https://doi.org/10.1007/s44244-024-00015-9 |
| work_keys_str_mv | AT alfianakbargozali modifiedgeneticalgorithmtosolveworkerassignmentproblemwithtimewindows |