云制造
计算机科学
云计算
强化学习
作业车间调度
分布式计算
调度(生产过程)
人工智能
工程类
地铁列车时刻表
运营管理
操作系统
作者
Zhen Chen,Linlin Zhang,Xiaohan Wang,Kunyu Wang
标识
DOI:10.1016/j.cie.2023.109053
摘要
Cloud Manufacturing (CMfg), as a service-oriented manufacturing mode, aims to provide consumers on-demand manufacturing services. The CMfg platform requires task scheduling technology to schedule manufacturing tasks efficiently, and improve resource utilization and customer satisfaction. Existing scheduling models for manufacturing tasks mainly consider maximizing the quality of service for customers but ignore the actual production execution, which will lead to low-quality execution or delayed delivery. To maximize customer satisfaction and balance production, this article studies a cloud–edge collaboration manufacturing task scheduling in CMfg (CETS). CETS refines manufacturing services deployed in the cloud to the factory process level, and schedules tasks according to the real-time production information on the edge side and manufacturing service information on the cloud side. Considering the dynamics of CETS and the complexity of state information in CETS, an attention-based deep reinforcement learning (DRL) algorithm is proposed to solve CETS. First, the CETS is mathematically represented and built as a partially observable Markov decision process. Second, on-policy maximum a posteriori policy optimization (V-MPO) with gated transformer-XL (GTrXL) named AV-MPO is developed. The effectiveness, training stability, generalizability, scalability, and robustness of AV-MPO are investigated. Rule-based algorithms and some state of art DRL algorithms, such as proximal policy optimization (PPO), soft actor-critic (SAC), and dueling deep q network (Dueling DQN), are compared with AV-MPO. The experimental results validate that AV-MPO can deal with the CETS problem more effectively.
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