Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning

Huagang Liang; Xiaoqian Wen; Yongkui Liu; Haifeng Zhang; Zhang Li; Lihui Wang

doi:10.1016/j.rcim.2020.101991

ScienceGate Book Chapters

JOURNAL ARTICLE

Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning

Huagang Liang Xiaoqian Wen Yongkui Liu Haifeng Zhang Zhang Li Lihui Wang

Year: 2020 Journal: Robotics and Computer-Integrated Manufacturing Vol: 67 Pages: 101991-101991 Publisher: Elsevier BV

DOI: 10.1016/j.rcim.2020.101991

Get Full-Text PDF Get Analytical Report

Keywords:

Cloud manufacturing Cloud computing Computer science Quality of service Adaptability Reinforcement learning Distributed computing Scalability Service composition Heuristics Granularity Artificial intelligence Computer network Database

Metrics

138

Cited By

16.03

FWCI (Field Weighted Citation Impact)

Refs

0.99

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Digital Transformation in Industry

Physical Sciences → Engineering → Industrial and Manufacturing Engineering

Flexible and Reconfigurable Manufacturing Systems

Physical Sciences → Engineering → Industrial and Manufacturing Engineering

Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning

Metrics

Citation History

Topics

Related Documents

Logistics-involved task scheduling in cloud manufacturing with offline deep reinforcement learning

Correlation-aware QoS modeling and manufacturing cloud service composition

Deep Reinforcement Learning for QoS-Aware IoT Service Composition: The PD3QND Approach

An efficient cloud manufacturing service composition approach using deep reinforcement learning

A hybrid teaching-learning-based optimization algorithm for QoS-aware manufacturing cloud service composition