Title: Cloud manufacturing service evaluation based on modular neural network

Authors: Shenghui Liu; Shuli Zhang; Chao Ma; Hongguo Zhang; Xing Zhang

Addresses: School of Software, Harbin University of Science and Technology, 150080, China ' School of Software, Harbin University of Science and Technology, 150080, China ' School of Software, Harbin University of Science and Technology, 150080, China ' School of Software, Harbin University of Science and Technology, 150080, China ' School of Software, Harbin University of Science and Technology, 150080, China

Abstract: In order to solve the evaluation problem of cloud manufacturing service and then minimise the time of choosing the best service, a comprehensive evaluation index system of cloud manufacturing services is constructed in the light of the characteristics of cloud manufacturing service. The service attributes, manufacturing attributes and network attributes are taken into account from two views of the online characteristics and the offline characteristics of cloud manufacturing service in the evaluation index system. And on this basis, a quantitative evaluation model based on modular neural network (MNN) is proposed. Firstly, the clustering algorithm subtractive-k-means is introduced and used to divide the task into several subtasks based on the idea of modularisation. Then, the subtasks are processed through the trained BP network. Finally, the self-adaptive genetic algorithm is used to optimise the integration weights of each subtask processing module. Combining with the cloud manufacturing evaluation index system, this modular neural network model can be used to effectively evaluate the cloud manufacturing service and help customers quickly select the best cloud manufacturing service.

Keywords: cloud manufacturing; evaluation model; modular neural network; MNN; clustering algorithm; self-adaptive genetic algorithm.

DOI: 10.1504/IJIMS.2018.091992

International Journal of Internet Manufacturing and Services, 2018 Vol.5 No.2/3, pp.204 - 219

Received: 07 Jul 2017
Accepted: 24 Oct 2017
Published online: 24 May 2018 *

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