Platform-enterprise collaborative scheduling in cloud manufacturing with deep reinforcement learning

Abstract

The core value of cloud manufacturing is to enable optimal allocation of manufacturing resources across enterprises in a wide-area environment. Scheduling is the key technology to realize the core value of cloud manufacturing. At present, large-scale enterprises are integrated in the cloud platform, which poses great difficulties for cloud manufacturing scheduling. How to perform collaborative scheduling of platform and enterprises is therefore a key question. Moreover, in the cloud manufacturing environment, distributed enterprises with autonomy on the edge side access their own resources to the cloud manufacturing service platform and carry out collaborative scheduling with the cloud manufacturing platform. Platform-enterprise collaborative scheduling provides support for large-scale resources and services within the cloud. Given this, the paper provides a platform-enterprise collaborative model that is adopted to study the scheduling problem of large-scale resources and services in cloud manufacturing. The model considers the platform-based service scheduling and enterprise-based resource scheduling. The collaborative scheduling mechanisms of the cloud service and enterprise resource are investigated. The former completes the scheduling of cloud services while collaborating on tasks with the latter, and the latter completes the scheduling of enterprise resources while delivering scheduling information to the former. Moreover, deep reinforcement learning (DRL) has been widely applied to cloud manufacturing scheduling. A platform-enterprise collaborative scheduling algorithm based on dueling deep [Formula: see text]-Network with prioritized replay (CE-PDDQN) is proposed. To evaluate the effectiveness of our proposed algorithm, this paper selects DQN and dueling DQN for experiments. The experimental results show that the CE-PDDQN algorithm can obtain a better scheduling scheme after training and learning. And the CE-PDDQN algorithm is adaptive and scalable.