S.I. : Machine Learning Applications for Self-Organized Wireless Networks
In view of the limitations of the linear model used in the traditional supply chain solution method, it cannot adapt to the dynamic supply chain network solution method in the multi-source big data environment of the Internet, and maps the dynamic supply chain into a network diagram model, and proposes e-commerce. Supply chain network model, based on this model, presents a semi-instance pattern detection method based on collaborative matrix decomposition, which is used to detect a semi-instantiated collaborative behaviour pattern in the supply chain network. According to the given collaborative behaviour model, the collaborative supply matrix decomposition method is first used to calculate the candidate supply chain of the personalized supply chain, and the degree of intimacy between node entities in the supply chain network is calculated. Using the A* graph search algorithm, a supply chain result candidate chain set is generated based on a personalized supply chain candidate set. According to personalized time, cost and other constraints, the final supply chain solution is tailored. The correctness, efficiency and accuracy of the method were verified by the e-commerce supply chain data set for apparel.
Supply chain Cooperative behaviour pattern Network efficiency Identification of important nodes
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The authors acknowledge the National Natural Science Foundation of China (Grant: 71402048) and Hubei society of social sciences (Grant: 2016101).
Wang TK, Zhang Q, Chong HY, Wang XY (2017) Integrated supplier selection framework in a resilient construction supply chain: an approach via analytic hierarchy process (AHP) and grey relational analysis (GRA). Sustainability 9(2):289CrossRefGoogle Scholar
Vahidi F, Ali Torabi S, Ramezankhani MJ (2018) Sustainable supplier selection and order allocation under operational and disruption risks. J Clean Prod 174:1351–1365CrossRefGoogle Scholar
Yazdani M, Chatterjee P, Zavadskas EK, Zolfani SH (2017) Integrated QFD–MCDM framework for green supplier selection. J Clean Prod 142(4):3728–3740CrossRefGoogle Scholar
Jain V, Sangaiah AK, Sakhuja S (2018) Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry. Neural Comput Appl 29(7):555–564CrossRefGoogle Scholar
Babbar C, Amin SH (2018) A multi-objective mathematical model integrating environmental concerns for supplier selection and order allocation based on fuzzy QFD in beverages industry. Expert Syst Appl 92:27–38CrossRefGoogle Scholar
Rajesh R, Ravi V (2015) Supplier selection in resilient supply chains: a grey relational analysis approach. J Clean Prod 86:343–359CrossRefGoogle Scholar
Liu T, Deng Y, Chan F (2018) Evidential supplier selection based on DEMATEL and game theory. Int J Fuzzy Syst 20(4):1321–1333CrossRefGoogle Scholar
Hofmann E (2017) Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect. Int J Prod Res 55(17):5108–5126CrossRefGoogle Scholar
Li XS, Tian YJ, Florentin S, Rajan A (2015) An extension collaborative innovation model in the context of big data. Int J Inf Technol Decis Mak 14(1):69–91CrossRefGoogle Scholar
Zhong RY, Chen X, Chen C, Huang Q (2015) Big data analytics for physical internet-based intelligent manufacturing shop floors. Int J Prod Res 55(9):2610–2621CrossRefGoogle Scholar
Chen Q, Preston DS, Swink M (2016) How the use of big data analytics affects value creation in supply chain management. J Manag Inf Syst 32(4):4–39CrossRefGoogle Scholar
Schoenherr T, Speier-Pero C (2015) Data science, predictive analytics, and big data in supply chain management: current state and future potential. J Bus Logist 36(1):120–132CrossRefGoogle Scholar
Ivanov D, Sokolov B, Raguinia EAD (2014) Integrated dynamic scheduling of material flows and distributed information services in collaborative cyber-physical supply networks. Int J Syst Sci Oper Logist 1(1):18–26Google Scholar