Journal
COMPUTERS & INDUSTRIAL ENGINEERING
Volume 99, Issue -, Pages 423-431Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2015.12.018
Keywords
Cloud manufacturing; Web service composition; Quality of service; Multi-objective optimization; Evolutionary algorithm
Funding
- General Program of the National Natural Science Foundation of China [71101103, 71201115]
- National Natural Science Fund for Distinguished Young Scholars of China [70925005]
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Cloud manufacturing, combining Web services via internet to a cooperative manufacturing system, has been an increasing popularity for global manufacturing. It will unlock the tremendous value in the massive amount of data being generated by the manufactories. The problem of QoS-aware Web service composition (QWSC), i.e., selecting appropriate service for each component of a service composition from a pool of functionally identical service to satisfy the users' end-to-end QoS constraints, is a core of the cloud manufacturing. A novel QWSC method by multi-objective optimization is proposed to help users to make a flexible decision. First of all, the problem of QWSC is formulated to a multi-objective optimization model Where either QoS performance or QoS risk (variance comparing to the user's QoS requirement) is the individual optimization objective. And then, an efficient epsilon-dominance multi-objective evolutionary algorithm (EDMOEA) is developed to solve the presented model. Finally, experimental results verify the effectiveness and efficiency of the proposed method for the large-scale QWSC problem. (C) 2015 Elsevier Ltd. All rights reserved.
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