Improving Industrial Production Efficiency: A Hybrid Approach to Dynamic Scheduling - A Case Study

doi:10.23940/ijpe.25.02.p5.104111

Abstract

Abstract: Efficient scheduling in industrial production remains a critical challenge, requiring innovative approaches to address modern manufacturing systems' growing complexity and dynamic nature. This paper presents a hybrid optimization approach combining genetic algorithm (GA) and multi-agent systems (MAS) to tackle the challenges of machine scheduling in complex production environments. The study highlights significant advancements in reducing downtime, idle time, and production time, improving efficiency and resource utilization. GA provides optimal task sequencing and scheduling, while MAS enables real-time collaboration and dynamic adjustments of maintenance schedules, ensuring system adaptability to operational changes. The results underscore the robustness and versatility of the GA-MAS strategy, offering a practical and innovative solution to enhance productivity in modern industrial systems.

Key words: multi-agent systems, genetic algorithm, dynamic efficient scheduling, optimization, performance

Meroua Sahraoui and Ahmed Bellaouar. Improving Industrial Production Efficiency: A Hybrid Approach to Dynamic Scheduling - A Case Study [J]. Int J Performability Eng, 2025, 21(2): 104-111.

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