Flexible Job Shop Scheduling Optimization Using Genetic Algorithm For Handling Dynamic Factors
This research introduces the Genetic Adaptive Scheduling System (GASS), a novel framework designed to optimize scheduling in Flexible Job Shop Scheduling Problems (FJSP). Due to its complexity, FJSP presents significant challenges stemming from machine flexibility, dynamic routing, and operation pr...
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Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)
2025-06-01
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| Series: | Journal of Applied Engineering and Technological Science |
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| Online Access: | http://journal.yrpipku.com/index.php/jaets/article/view/5784 |
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| author | Masmur Tarigan Ford Lumban Gaol Tuga Mauritsius Widodo Budiharto |
| author_facet | Masmur Tarigan Ford Lumban Gaol Tuga Mauritsius Widodo Budiharto |
| author_sort | Masmur Tarigan |
| collection | DOAJ |
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This research introduces the Genetic Adaptive Scheduling System (GASS), a novel framework designed to optimize scheduling in Flexible Job Shop Scheduling Problems (FJSP). Due to its complexity, FJSP presents significant challenges stemming from machine flexibility, dynamic routing, and operation precedence constraints. GASS addresses these challenges by incorporating real-time, dynamic data, enabling the system to adapt to machine downtimes, fluctuating job priorities, and process variability. Leveraging advanced genetic algorithm techniques, GASS integrates enhanced mutation and selection processes that dynamically adjust setup times, prioritize urgent tasks, and balance machine workloads to minimize makespan effectively. Empirical results demonstrate that GASS achieves up to a 45.3% reduction in makespan within the flexible packaging industry, showcasing its ability to enhance scheduling efficiency and adaptability. The research highlights the system’s scalability and potential applicability across diverse industries, including printing, electronics, pharmaceuticals, and food manufacturing, where operational flexibility and efficiency are critical. By bridging existing gaps and integrating real-time constraints into scheduling models, GASS provides practical solutions for modern manufacturing environments. The findings contribute to the advancement of optimization techniques in FJSP, offering valuable insights for researchers and practitioners seeking efficient, scalable, and adaptive scheduling systems.
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| format | Article |
| id | doaj-art-6655cf559ced44be8ff4ee7205c9d91f |
| institution | DOAJ |
| issn | 2715-6087 2715-6079 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI) |
| record_format | Article |
| series | Journal of Applied Engineering and Technological Science |
| spelling | doaj-art-6655cf559ced44be8ff4ee7205c9d91f2025-08-20T03:10:41ZengYayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)Journal of Applied Engineering and Technological Science2715-60872715-60792025-06-016210.37385/jaets.v6i2.5784Flexible Job Shop Scheduling Optimization Using Genetic Algorithm For Handling Dynamic FactorsMasmur Tarigan0Ford Lumban Gaol1Tuga Mauritsius2Widodo Budiharto3Esa Unggul UniversityDepartment of Doctor of Computer Science, BINUS - Graduate Program, Bina Nusantara University, Jakarta, IndonesiaDepartment of Information System Management, Bina Nusantara University, Jakarta, IndonesiaComputer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia This research introduces the Genetic Adaptive Scheduling System (GASS), a novel framework designed to optimize scheduling in Flexible Job Shop Scheduling Problems (FJSP). Due to its complexity, FJSP presents significant challenges stemming from machine flexibility, dynamic routing, and operation precedence constraints. GASS addresses these challenges by incorporating real-time, dynamic data, enabling the system to adapt to machine downtimes, fluctuating job priorities, and process variability. Leveraging advanced genetic algorithm techniques, GASS integrates enhanced mutation and selection processes that dynamically adjust setup times, prioritize urgent tasks, and balance machine workloads to minimize makespan effectively. Empirical results demonstrate that GASS achieves up to a 45.3% reduction in makespan within the flexible packaging industry, showcasing its ability to enhance scheduling efficiency and adaptability. The research highlights the system’s scalability and potential applicability across diverse industries, including printing, electronics, pharmaceuticals, and food manufacturing, where operational flexibility and efficiency are critical. By bridging existing gaps and integrating real-time constraints into scheduling models, GASS provides practical solutions for modern manufacturing environments. The findings contribute to the advancement of optimization techniques in FJSP, offering valuable insights for researchers and practitioners seeking efficient, scalable, and adaptive scheduling systems. http://journal.yrpipku.com/index.php/jaets/article/view/5784Flexible Job Shop SchedulingGenetic Adaptive Scheduling SystemDynamic Scheduling OptimizationManufacturing Process EfficiencyReal-Time Production Scheduling |
| spellingShingle | Masmur Tarigan Ford Lumban Gaol Tuga Mauritsius Widodo Budiharto Flexible Job Shop Scheduling Optimization Using Genetic Algorithm For Handling Dynamic Factors Journal of Applied Engineering and Technological Science Flexible Job Shop Scheduling Genetic Adaptive Scheduling System Dynamic Scheduling Optimization Manufacturing Process Efficiency Real-Time Production Scheduling |
| title | Flexible Job Shop Scheduling Optimization Using Genetic Algorithm For Handling Dynamic Factors |
| title_full | Flexible Job Shop Scheduling Optimization Using Genetic Algorithm For Handling Dynamic Factors |
| title_fullStr | Flexible Job Shop Scheduling Optimization Using Genetic Algorithm For Handling Dynamic Factors |
| title_full_unstemmed | Flexible Job Shop Scheduling Optimization Using Genetic Algorithm For Handling Dynamic Factors |
| title_short | Flexible Job Shop Scheduling Optimization Using Genetic Algorithm For Handling Dynamic Factors |
| title_sort | flexible job shop scheduling optimization using genetic algorithm for handling dynamic factors |
| topic | Flexible Job Shop Scheduling Genetic Adaptive Scheduling System Dynamic Scheduling Optimization Manufacturing Process Efficiency Real-Time Production Scheduling |
| url | http://journal.yrpipku.com/index.php/jaets/article/view/5784 |
| work_keys_str_mv | AT masmurtarigan flexiblejobshopschedulingoptimizationusinggeneticalgorithmforhandlingdynamicfactors AT fordlumbangaol flexiblejobshopschedulingoptimizationusinggeneticalgorithmforhandlingdynamicfactors AT tugamauritsius flexiblejobshopschedulingoptimizationusinggeneticalgorithmforhandlingdynamicfactors AT widodobudiharto flexiblejobshopschedulingoptimizationusinggeneticalgorithmforhandlingdynamicfactors |