ConDRL-JSP: A Contrastive and Reinforcement Learning-Based Framework for JSP

In industries like manufacturing, the Job Shop Scheduling Problem JSP , a classic NP-hard combinatorial optimization problem, faces numerous challenges. Traditional solution methods are restricted by specific scenarios and suffer from high computational complexity, while constructive-based methods often exhibit low sample efficiency and poor generalization ability. To address these limitations, this paper proposes a novel job shop scheduling framework that integrates contrastive learning and reinforcement learning, complemented by a curriculum learning strategy to enhance model training and significantly improve production scheduling efficiency.A key motivation for incorporating contrastive learning is to address the weak feature discrimination and limited data mining capabilities of traditional reinforcement learning methods in JSP. By leveraging contrastive learning, the framework enhances the model’s ability to extract discriminative features from complex scheduling states, enabling more effective decision-making in diverse and large-scale scenarios. Additionally, the framework adopts a curriculum learning strategy to guide the model through a progressive learning process. This strategy dynamically adjusts the difficulty of training tasks based on the model’s performance, starting with simpler instances and gradually advancing to more complex ones. This approach not only improves the model’s generalization ability but also helps avoid local optima, ensuring robust and efficient scheduling solutions.Experimental results demonstrate that the proposed framework achieves significant improvements in scheduling quality, reducing the makespan and approaching optimal solutions across various benchmark datasets. The integration of contrastive learning and curriculum learning provides a powerful and adaptable solution to the challenges of JSP, offering a promising direction for future research in combinatorial optimization.