A Deep-Reinforcement-Learning-Based Optimization Approach for Real-Time Scheduling in Cloud Manufacturing

Abstract

Resource scheduling problems (RSPs) in cloud manufacturing (CMfg) often manifest as dynamic scheduling problems in which scheduling strategies depend on real-time environments and demands. Generally, multiple resources in the CMfg scheduling process cause difficulties in system modeling. To solve this problem, we propose Sharer, a deep reinforcement learning (DRL)-based method that converts scheduling problems with multiple resources into one learning target and learns effective strategies automatically. Our preliminary results show that Sharer is comparable to the latest heuristics, adapts to different conditions, converges quickly, and subsequently learns wise strategies.