Service Composition and Optimal Selection in Cloud Manufacturing: State-of-the-Art and Research Challenges

Increasing interest in the field of Cloud Manufacturing (CMfg) has been witnessed over the last few years. This study aims to identify current and state-of-the-art techniques and to synthesize quality attributes, objectives, and evaluation methodologies for service composition and optimal selection (SCOS) in the field of CMfg. We used a systematic literature review (SLR) methodology for a thorough analysis of 46 shortlisted primary studies, from a total of 5872 accumulated studies from ten electronic databases. NVivo analysis software was used for data coding and qualitative analysis. A review scope was primarily devised based on research goals, and to uncover potential search strings; a pilot study was formulated. Secondarily, research identification, key data extraction, and deductive coding-based data analysis were performed. Multi-variant distribution approaches were adopted for data categorization. We found that the research in this domain has increased due to the rapid manufacturing urge. Although a few studies were based on industrial evaluations; however, scientific and empirically validated methodologies are still needed in this domain. This study lays an overview of SCOS in the field of CMfg and enlightens the identified future research areas.