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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputasi JUTEKIN (Jurnal Manajemen Informatika) Jurnal Teknologi Informasi dan Terapan (J-TIT) Journal of Applied Engineering and Technological Science (JAETS) Bit (Fakultas Teknologi Informasi Universitas Budi Luhur)
Tarigan, Masmur
Universitas Esa Unggul
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Flexible Job Shop Scheduling Optimization Using Genetic Algorithm For Handling Dynamic Factors Masmur Tarigan; Ford Lumban Gaol; Alexander AS Gunawan; Widodo Budiharto
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 2 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i2.5784

Abstract

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.
Co-Authors Adven Kristianata Ahmad Johadi Alexander AS Gunawan Budi Tjahjono Dini Handayani Ford Lumban Gaol Muhamad Bahrul Ulum Nanda Pratama Nizirwan Anwar Rizki Ramadhan Sandri Alfarisi Widodo Budiharto Yudi Susanto