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SFLA-based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 53 |
| Year of Publication: 2024 |
| Authors: Duc Hoang Nguyen |
10.5120/ijca2024924222
|
Duc Hoang Nguyen . SFLA-based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production. International Journal of Computer Applications. 186, 53 ( Dec 2024), 57-63. DOI=10.5120/ijca2024924222
Abstract
Effective task allocation and workload balancing are critical challenges in garment production, particularly in systems that involve hanger transportation for moving garments between workstations. This paper presents a novel method for optimizing assembly line balancing by integrating the Shuffled Frog Leaping Algorithm (SFLA) with a task grouping strategy based on skill level, machine type, and precedence constraints, within a Master-Slave control framework for hanger transportation systems (HTS). The Master controller leverages SFLA to globally optimize task assignments aiming to minimize the number of stations, balance workloads, and reduce idle time, while ensuring task precedence and grouping requirements are met. Meanwhile, Slave controllers execute tasks locally and provide real-time feedback, facilitating dynamic adjustments based on system conditions. Simulations based on real-world garment assembly data demonstrate that the SFLA-based method significantly improves task allocation efficiency, reduces the number of stations, and enhances throughput compared to traditional approaches. This method increases the flexibility and adaptability of garment production lines, offering a robust solution for complex, dynamic manufacturing environments.
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