A hybrid Genetic Algorithm and Fuzzy Logic approach to ergonomic design of workstations in metal casting operations
The metal casting industry faces significant challenges in balancing productivity with worker safety and well-being. Hazardous working conditions, including high temperatures, exposure to gases, and repetitive motions, increase the risk of injuries and fatigue. 1 This study proposes a novel hybrid a...
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| Format: | Article |
| Language: | Indonesian |
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Universitas Pembangunan Nasional "Veteran" Yogyakarta
2024-12-01
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| Series: | OPSI |
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| Online Access: | http://jurnal.upnyk.ac.id/index.php/opsi/article/view/13943 |
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| author | Intan Berlianty Irwan Soejanto Indun Titisariwati Favian Ersanta Andhika Putra Wahyu Tri Utami Mohamad Kamil Insani |
| author_facet | Intan Berlianty Irwan Soejanto Indun Titisariwati Favian Ersanta Andhika Putra Wahyu Tri Utami Mohamad Kamil Insani |
| author_sort | Intan Berlianty |
| collection | DOAJ |
| description | The metal casting industry faces significant challenges in balancing productivity with worker safety and well-being. Hazardous working conditions, including high temperatures, exposure to gases, and repetitive motions, increase the risk of injuries and fatigue. 1 This study proposes a novel hybrid approach that integrates Genetic Algorithm (GA) and Fuzzy Logic (FL) to optimize workstation ergonomics. The system utilizes real-time data from sensors to evaluate ergonomic factors such as worker posture, fatigue levels, and environmental conditions. Fuzzy Logic processes this data, while GA optimizes the system's parameters for enhanced accuracy and adaptability. Experimental results demonstrated significant improvements, including a 25% reduction in worker fatigue, a 30% improvement in air quality compliance, and a 35% decrease in ergonomic risks. Real-time adjustments, such as desk height modifications and improved ventilation, effectively enhanced worker safety and comfort. This innovative approach offers a scalable and reliable solution for improving ergonomics in dynamic industrial environments, contributing to both worker well-being and operational efficiency. Future research could further enhance the system by incorporating machine learning for improved predictive capabilities and expanded optimization of ergonomic parameters. |
| format | Article |
| id | doaj-art-4c78bcf601c143aeaa81b2c93f8ca879 |
| institution | Kabale University |
| issn | 1693-2102 2686-2352 |
| language | Indonesian |
| publishDate | 2024-12-01 |
| publisher | Universitas Pembangunan Nasional "Veteran" Yogyakarta |
| record_format | Article |
| series | OPSI |
| spelling | doaj-art-4c78bcf601c143aeaa81b2c93f8ca8792025-01-30T00:34:38ZindUniversitas Pembangunan Nasional "Veteran" YogyakartaOPSI1693-21022686-23522024-12-0117240342010.31315/opsi.v17i2.139435957A hybrid Genetic Algorithm and Fuzzy Logic approach to ergonomic design of workstations in metal casting operationsIntan Berlianty0Irwan SoejantoIndun TitisariwatiFavian Ersanta Andhika PutraWahyu Tri UtamiMohamad Kamil InsaniUniversitas Pembangunan Nasional Veteran YogyakartaThe metal casting industry faces significant challenges in balancing productivity with worker safety and well-being. Hazardous working conditions, including high temperatures, exposure to gases, and repetitive motions, increase the risk of injuries and fatigue. 1 This study proposes a novel hybrid approach that integrates Genetic Algorithm (GA) and Fuzzy Logic (FL) to optimize workstation ergonomics. The system utilizes real-time data from sensors to evaluate ergonomic factors such as worker posture, fatigue levels, and environmental conditions. Fuzzy Logic processes this data, while GA optimizes the system's parameters for enhanced accuracy and adaptability. Experimental results demonstrated significant improvements, including a 25% reduction in worker fatigue, a 30% improvement in air quality compliance, and a 35% decrease in ergonomic risks. Real-time adjustments, such as desk height modifications and improved ventilation, effectively enhanced worker safety and comfort. This innovative approach offers a scalable and reliable solution for improving ergonomics in dynamic industrial environments, contributing to both worker well-being and operational efficiency. Future research could further enhance the system by incorporating machine learning for improved predictive capabilities and expanded optimization of ergonomic parameters.http://jurnal.upnyk.ac.id/index.php/opsi/article/view/13943ergonomic optimizationfuzzy logicgenetic algorithmworkstation designmetal casting operations |
| spellingShingle | Intan Berlianty Irwan Soejanto Indun Titisariwati Favian Ersanta Andhika Putra Wahyu Tri Utami Mohamad Kamil Insani A hybrid Genetic Algorithm and Fuzzy Logic approach to ergonomic design of workstations in metal casting operations OPSI ergonomic optimization fuzzy logic genetic algorithm workstation design metal casting operations |
| title | A hybrid Genetic Algorithm and Fuzzy Logic approach to ergonomic design of workstations in metal casting operations |
| title_full | A hybrid Genetic Algorithm and Fuzzy Logic approach to ergonomic design of workstations in metal casting operations |
| title_fullStr | A hybrid Genetic Algorithm and Fuzzy Logic approach to ergonomic design of workstations in metal casting operations |
| title_full_unstemmed | A hybrid Genetic Algorithm and Fuzzy Logic approach to ergonomic design of workstations in metal casting operations |
| title_short | A hybrid Genetic Algorithm and Fuzzy Logic approach to ergonomic design of workstations in metal casting operations |
| title_sort | hybrid genetic algorithm and fuzzy logic approach to ergonomic design of workstations in metal casting operations |
| topic | ergonomic optimization fuzzy logic genetic algorithm workstation design metal casting operations |
| url | http://jurnal.upnyk.ac.id/index.php/opsi/article/view/13943 |
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