Blockchain-based cloud manufacturing platforms: A novel idea for service composition in XaaS paradigm

Radmanesh, Seyyed-Alireza; Haji, Alireza; Valilai, Omid Fatahi

doi:10.7717/peerj-cs.743

Cited by 9 publications

(1 citation statement)

References 51 publications

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“…Hasan et al [ 25 ] proposed an improved CMfg service platform architecture framework that enables mass customization services from remote clients and gives some implementation details. Radmanesh et al [ 26 ], after combining with blockchain technology, proposed a CMfg service platform which considers distributed service combinations that can effectively solve the operation management problem in industrial applications. Pan et al [ 27 ] designed a pricing model for CMfg platform based on the bilateral market theory research for the pricing problem under the CMfg model, and by solving the model, the optimal pricing strategy for maximizing the profit of the CMfg platform was obtained.…”

Section: Related Workmentioning

confidence: 99%

Research on cloud manufacturing service recommendation based on graph neural network

Li,

Shi,

Shi

et al. 2023

PLoS ONE

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There are an increasing number of manufacturing service resources appeared on the cloud manufacturing (CMfg) service platform recently, which leads to a serious information overloading problem to the enterprises that need these resources. To tackle this problem, a graph neural network-based recommendation method for CMfg service resources is proposed, which effectively overcomes some limitations of the traditional recommendation methods. Specifically, we first use different similarity calculation methods (e.g., Cosine similarity, Pearson correlation coefficient, etc.) to calculate the similarities between different resources based on the feature information of CMfg service resources. A resource graph dataset is accordingly established. A graph neural network is then used to perform representation learning of nodes in these graphs, obtaining the vector representations of these nodes. Finally, new links that may appear in a graph are predicted by performing dot product calculations on these nodes’ vector representations. And these links can be used to recommend suitable resources. Experiments mainly show that (i) the proposed method obtains better link prediction accuracy compared with that of other link prediction algorithms; (ii) when the network density used for training is relatively high, the predictive performance of the proposed method is improved significantly. Our method can shed light on how to choose suitable CMfg service resources from the CMfg service platform.

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Section: Related Workmentioning

confidence: 99%