Review of Cloud Service Composition for Intelligent Manufacturing

TL;DR

This paper reviews cloud service optimization in intelligent manufacturing, summarizing processes, defining key indicators, classifying algorithms, and highlighting future research directions for sustainable platform development.

Contribution

It introduces a comprehensive framework for cloud service optimization, including standardized indicators and a comparison of heuristic and reinforcement learning algorithms.

Findings

Defined 11 optimization indicators considering three-party participants.

Compared heuristic and reinforcement learning algorithms for service optimization.

Summarized current research hotspots and future trends in the field.

Abstract

Intelligent manufacturing is a new model that uses advanced technologies such as the Internet of Things, big data, and artificial intelligence to improve the efficiency and quality of manufacturing production. As an important support to promote the transformation and upgrading of the manufacturing industry, cloud service optimization has received the attention of researchers. In recent years, remarkable research results have been achieved in this field. For the sustainability of intelligent manufacturing platforms, in this paper we summarize the process of cloud service optimization for intelligent manufacturing. Further, to address the problems of dispersed optimization indicators and nonuniform/unstandardized definitions in the existing research, 11 optimization indicators that take into account three-party participant subjects are defined from the urgent requirements of the sustainable development of intelligent manufacturing platforms. Next, service optimization algorithms are classified into two categories, heuristic and reinforcement learning. After comparing the two categories, the current key techniques of service optimization are targeted. Finally, research hotspots and future research trends of service optimization are summarized.