Content area
The rapid advancement of Industry 4.0 has revolutionized manufacturing, shifting production from centralized control to decentralized, intelligent systems. Smart factories are now expected to achieve high adaptability and resource efficiency, particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands. To address the challenges of dynamic task allocation, uncertainty, and real-time decision-making, this paper proposes Pathfinder, a deep reinforcement learning-based scheduling framework. Pathfinder models scheduling data through three key matrices: execution time (the time required for a job to complete), completion time (the actual time at which a job is finished), and efficiency (the performance of executing a single job). By leveraging neural networks, Pathfinder extracts essential features from these matrices, enabling intelligent decision-making in dynamic production environments. Unlike traditional approaches with fixed scheduling rules, Pathfinder dynamically selects from ten diverse scheduling rules, optimizing decisions based on real-time environmental conditions. To further enhance scheduling efficiency, a specialized reward function is designed to support dynamic task allocation and real-time adjustments. This function helps Pathfinder continuously refine its scheduling strategy, improving machine utilization and minimizing job completion times. Through reinforcement learning, Pathfinder adapts to evolving production demands, ensuring robust performance in real-world applications. Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches, offering improved coordination and efficiency in smart factories. By integrating deep reinforcement learning, adaptable scheduling strategies, and an innovative reward function, Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.