Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61236
Title: Composition of resource-service chain for cloud manufacturing
Authors: Li, H
Chan, KCC 
Liang, M
Luo, X
Keywords: Business process
Cloud Manufacturing (CMfg)
Manufacturing resource composition
Resource-service chain
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on industrial informatics, 2016, v. 12, no. 1, 7335620, p. 211-219 How to cite?
Journal: IEEE transactions on industrial informatics 
Abstract: In distributed manufacturing systems, manufacturing resource composition is one of the most important problems. This is because efficiency of resource selection and resource utilization can all be improved if it is tackled well. However, most of the existing methods neglect temporal relationship between resources. This leads to an inefficient use of resources, because all resources have to be kept available before a business process is started. A temporal composition of resources is more suitable, as it expresses the scheduling and the flow of servicing to a business process. Therefore, resource services invoked in sequential order are called the resource-service chain (RSC), in view that distributed resources are encapsulated into cloud services in a cloud manufacturing (CMfg) environment. We propose an approach, called RSC composition algorithm (RSCCA) that can better cope with the temporal relationship between the resource services in a business process. Specifically, a two-stage composition method based on the degrees of dependency between resource services in workflow is proposed. To begin, in the build-time stage algorithm, RSCCA resolves initial compositions based on task relatedness and temporal dependencies between resource services, and then calculates the usage frequencies of ICs by mining workflow log at workflow runtime stage. Based on this, RSCCA can compose individual resource services as more than sets, especially as chains, allowing flow directions and dynamics to be considered. RSCCA has been tested with different data sets and the results show that it can be very promising.
URI: http://hdl.handle.net/10397/61236
ISSN: 1551-3203
EISSN: 1941-0050
DOI: 10.1109/TII.2015.2503126
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