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The promotion strategy of supply chain flexibility based on deep belief network

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Abstract

Supply chain flexibility is the processing ability of the enterprize to deal with the uncertain environment of supply and demand. In this paper, we consider the supply side (node interrupt) and demand side (demand fluctuations) under uncertain environment. By using the deep belief network (DBN), which is composed of multilayer Restricted Boltzmann Machine (RBM), it establishes the supply chain flexibility network with optimization of the transfer node and flow. The deep belief network is trained by the data of large manufacturing enterprize, compared with the traditional neural network (MLR, BP and GA). The results show that the deep belief network model overcomes the shortcomings of the traditional neural networks, such as easy to fall into local optimum, long training time and low function fitting degree, and it has higher prediction accuracy. This network model based on the deep belief network can promote the supply chain flexibility more, when supply and demand fluctuations occur.

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Acknowledgments

This work was partially supported by Key projects for the Chinese Ministry of Education (No. 15JZD021), and Tianjin higher education innovation team training program (No. TD12-5013).

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Authors and Affiliations

  1. School of Management, Tianjin University of Technology, Tianjin, People’s Republic of China

    Fanhui Kong & Jian Li

  2. College of Management and Economics, Tianjin University, Tianjin, People’s Republic of China

    Jian Li

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  1. Fanhui Kong
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  2. Jian Li
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Correspondence to Fanhui Kong.

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Kong, F., Li, J. The promotion strategy of supply chain flexibility based on deep belief network. Appl Intell 48, 1394–1405 (2018). https://doi.org/10.1007/s10489-018-1138-x

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