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dc.contributor.authorZhang, Fangweien_US
dc.contributor.authorYe, Junen_US
dc.contributor.authorHan, Bingen_US
dc.contributor.authorSun, Jingen_US
dc.contributor.authorZhang, Limingen_US
dc.identifier.citationZhang, F., Ye, J., Han, B., Sun, J. & Zhang, L. (2022). Design of interconnected warehouse and routing optimization by BP genetic neural network algorithm. Mathematical Problems in Engineering, 2022, 5400847-.
dc.description.abstractWith the continuous progress of the chemical industry, warehouse design needs to be diversified on account of the increasing complex and multitudinous perilous chemicals. In this situation, this study projects the conception of the interconnected warehouse. By taking the storage points as the quantity and the path as the variable, this study establishes a quadratic allocation model on the operations of this novel kind of warehouse. Then, an improved neural network algorithm is proposed to ascertain the optimal solution. The innovation of this study is that it releases the space resources of the classic dangerous goods warehouse and improves the operational efficiency of the dangerous goods warehouse under the premise of ensuring safety. Finally, the proposed model and algorithm is tested and verified with a data of Shanghai Lingang dangerous Material Warehouse. The empirical research demonstrates that the interconnected warehouse has ideal performance for lifting the handling efficiency on the basis of ensuring safety.en_US
dc.relation.ispartofMathematical Problems in Engineeringen_US
dc.rights© 2022 Fangwei Zhang et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleDesign of interconnected warehouse and routing optimization by BP genetic neural network algorithmen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.versionPublished versionen_US
dc.subject.keywordsAllocation Modelen_US
dc.subject.keywordsGenetic Neural Networken_US
dc.description.acknowledgementThe Fangwei Zhang’s work was partially supported by Shanghai Pujiang Program (Grant no. 2019PJC062), the Natural Science Foundation of Shandong Province (Grant no. ZR2021MG003), the Research Project on Undergraduate Teaching Reform of Higher Education in Shandong Province (Grant no. Z2021046), the National Natural Science Foundation of China (Grant no. 51508319), the Nature and Science Fund from Zhejiang Province Ministry of Education (Grant no. Y201327642).en_US
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