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.
Highlights
Cloud manufacturing (CMfg) is a new manufacturing model developed from existing technology and supported by cloud computing, Internet of Things (IoT), and virtualization [1]
We propose a resource scheduling problem in CMfg, which is a multiobjective optimization problem in terms of minimizing operation time, maximizing resource utilization, and minimizing overhead
FRAMEWORK OF CMFG We introduce the framework of our CMfg platform, which is based on a user demand layer, distributed cloud layer, and manufacturing layer
Summary
Cloud manufacturing (CMfg) is a new manufacturing model developed from existing technology and supported by cloud computing, Internet of Things (IoT), and virtualization [1]. The allocation of limited production resources to different tasks improves manufacturing efficiency and resource utilization [2]. Resource scheduling plays a key role in improving the performance of CMfg systems. CMfg platforms can effectively leverage distributed manufacturing resources (DMRs) in regional enterprise clusters. FRAMEWORK OF CMFG We introduce the framework of our CMfg platform, which is based on a user demand layer, distributed cloud layer, and manufacturing layer. In the user demand layer, many demands (e.g., part production, product processing, assembly lines, and material blending) are raised in discrete timesteps. These demands from different users are always heterogeneous, i.e., they hold various dimensions of manufacturing attributes. Our system should be able to autonomously extract the tasks that have common characteristics among the tasks to be solved
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