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Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem
Recent advancements in the flexible job-shop scheduling problem (FJSSP) are primarily based on deep reinforcement learning (DRL) due to its ability to generate high-quality, real-time solutions.
Solving the flexible job-shop scheduling problem through an enhanced deep reinforcement learning approach
In scheduling problems common in the industry and various real-world scenarios, responding in real-time to disruptive events is essential.
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