  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.
