We study the Dynamic Flexible Job Shop Scheduling Problem (DFJSSP), which is a well-known combinatorial optimization problem where new jobs arrive dynamically over time and have to be scheduled on a sequence of machines. Unlike most works that look at makespan or related criteria, we study a new cost minimization objective for the DFJSSP. We propose a new deep reinforcement learning method for scheduling and show that our method outperforms benchmark methods.
Deep Reinforcement Learning for the Dynamic Flexible Job Shop Scheduling Problem with Cost Optimization
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