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Service optimal selection and composition in cloud manufacturing: a comprehensive survey

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Abstract

With the rapid development of cloud manufacturing (CMfg) as a new service-oriented manufacturing paradigm, a considerable progress has been made in research of different aspects of it. One of the most challenging topics of interest has been service composition and optimal selection (SCOS) problem. Since CMfg is aiming towards sharing and collaborating among distributed manufacturing resources and capabilities, selecting and combining these services into a composite service to meet the user’s requirements while keeping up the optimal service performances is gaining higher emphasis. As a result, a comprehensive survey of research to date on this NP-hard problem becomes highly desirable. In this paper, first we summarize the recent advancements in CMfg and categorize them into six main areas in a brief but concise way. Then, after a short explanation of the SCOS problem, existing research work around it has been investigated and discussed in detail from the viewpoint of selection criteria, algorithms, optimization functions, correlation consideration, mapping approaches between subtasks and services, and dynamic composition. The goal of this article is to provide a comprehensive highlight for researchers who are inspired to explore work in the related areas and acquaint them with related research work done to date.

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  1. Department of Mechanical Engineering & Center for Advanced Manufacturing and Lean Systems, The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA

    Hamed Bouzary & F. Frank Chen

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  1. Hamed Bouzary
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  2. F. Frank Chen
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Correspondence to F. Frank Chen.

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Bouzary, H., Frank Chen, F. Service optimal selection and composition in cloud manufacturing: a comprehensive survey. Int J Adv Manuf Technol 97, 795–808 (2018). https://doi.org/10.1007/s00170-018-1910-4

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