Zapisane w:
| Główni autorzy: | , |
|---|---|
| Format: | Recurso digital |
| Język: | angielski |
| Wydane: |
Zenodo
2025
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| Hasła przedmiotowe: | |
| Dostęp online: | https://doi.org/10.5281/zenodo.16734859 |
| Etykiety: |
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Spis treści:
- <p>In today’s highly competitive manufacturing landscape, optimizing job shop scheduling has become vital for maximizing operational efficiency and minimizing production delays. This study explores the dual challenge of makespan minimization and efficient buffer management within job shop environments. By integrating a hybrid metaheuristic approach combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), we propose an intelligent scheduling framework that not only reduces the overall makespan but also enhances the utilization of intermediate buffers across machines. The experimental results, validated through benchmark datasets and real-time shop floor simulations, demonstrate significant improvements in throughput, machine utilization, and flow consistency. Our approach outperforms traditional heuristics by dynamically adjusting buffer capacities and job sequencing based on system feedback, paving the way for more resilient and adaptive manufacturing operations.</p>