Logistics Distribution Route Optimization Model Based on Recursive Fuzzy Neural Network Algorithm
In recent years, more and more attention has been paid to the utilization of data and information in the logistics distribution path optimization system of e-commerce, but it is difficult to have scientific guarantee in the process of determining the optimal distribution path scheme of e-commerce. H...
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| Published in | Computational intelligence and neuroscience Vol. 2021; no. 1; p. 3338840 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
United States
Hindawi
2021
John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1687-5265 1687-5273 1687-5273 |
| DOI | 10.1155/2021/3338840 |
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| Abstract | In recent years, more and more attention has been paid to the utilization of data and information in the logistics distribution path optimization system of e-commerce, but it is difficult to have scientific guarantee in the process of determining the optimal distribution path scheme of e-commerce. How to realize the optimization and adaptive setting of distribution path by using intelligent algorithm has become a hot spot. To battle these issues, this paper studies the logistics distribution path optimization model based on recursive fuzzy neural network algorithm. This paper analyses the research status of logistics distribution path determination scheme and applies the recursive fuzzy neural network algorithm in the selection of e-commerce logistics distribution path scheme. The experimental results show that the recursive fuzzy neural network algorithm can realize the optimization of e-commerce logistics distribution path, and the best distribution route can be made according to the characteristic difference of logistics distribution route, and its distribution accuracy can reach more than 97%. |
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| AbstractList | In recent years, more and more attention has been paid to the utilization of data and information in the logistics distribution path optimization system of e-commerce, but it is difficult to have scientific guarantee in the process of determining the optimal distribution path scheme of e-commerce. How to realize the optimization and adaptive setting of distribution path by using intelligent algorithm has become a hot spot. To battle these issues, this paper studies the logistics distribution path optimization model based on recursive fuzzy neural network algorithm. This paper analyses the research status of logistics distribution path determination scheme and applies the recursive fuzzy neural network algorithm in the selection of e-commerce logistics distribution path scheme. The experimental results show that the recursive fuzzy neural network algorithm can realize the optimization of e-commerce logistics distribution path, and the best distribution route can be made according to the characteristic difference of logistics distribution route, and its distribution accuracy can reach more than 97%.In recent years, more and more attention has been paid to the utilization of data and information in the logistics distribution path optimization system of e-commerce, but it is difficult to have scientific guarantee in the process of determining the optimal distribution path scheme of e-commerce. How to realize the optimization and adaptive setting of distribution path by using intelligent algorithm has become a hot spot. To battle these issues, this paper studies the logistics distribution path optimization model based on recursive fuzzy neural network algorithm. This paper analyses the research status of logistics distribution path determination scheme and applies the recursive fuzzy neural network algorithm in the selection of e-commerce logistics distribution path scheme. The experimental results show that the recursive fuzzy neural network algorithm can realize the optimization of e-commerce logistics distribution path, and the best distribution route can be made according to the characteristic difference of logistics distribution route, and its distribution accuracy can reach more than 97%. In recent years, more and more attention has been paid to the utilization of data and information in the logistics distribution path optimization system of e-commerce, but it is difficult to have scientific guarantee in the process of determining the optimal distribution path scheme of e-commerce. How to realize the optimization and adaptive setting of distribution path by using intelligent algorithm has become a hot spot. To battle these issues, this paper studies the logistics distribution path optimization model based on recursive fuzzy neural network algorithm. This paper analyses the research status of logistics distribution path determination scheme and applies the recursive fuzzy neural network algorithm in the selection of e-commerce logistics distribution path scheme. The experimental results show that the recursive fuzzy neural network algorithm can realize the optimization of e-commerce logistics distribution path, and the best distribution route can be made according to the characteristic difference of logistics distribution route, and its distribution accuracy can reach more than 97%. |
| Audience | Academic |
| Author | Liu, Binbin |
| AuthorAffiliation | 2 Department of Business Administration, Jeonbuk National University, Jeonju 54896, Republic of Korea 1 Department of Business Administration, Suqian University, Suqian 223800, China |
| AuthorAffiliation_xml | – name: 1 Department of Business Administration, Suqian University, Suqian 223800, China – name: 2 Department of Business Administration, Jeonbuk National University, Jeonju 54896, Republic of Korea |
| Author_xml | – sequence: 1 givenname: Binbin orcidid: 0000-0001-5156-2108 surname: Liu fullname: Liu, Binbin organization: Department of Business AdministrationSuqian UniversitySuqian 223800China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34777491$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1155_2022_3696817 crossref_primary_10_1155_2023_9825906 crossref_primary_10_3934_mbe_2023031 crossref_primary_10_1155_2022_2953205 crossref_primary_10_3390_math11122734 crossref_primary_10_1155_2022_8468438 crossref_primary_10_1016_j_ject_2023_11_002 |
| Cites_doi | 10.1186/s13677-020-00174-x 10.1016/j.indmarman.2019.10.009 10.3390/su12083381 10.1007/s00500-020-05527-x 10.3233/jifs-179802 10.1088/1742-6596/1811/1/012010 10.1007/s12065-020-00365-0 10.14743/apem2018.4.304 10.1016/j.tre.2019.04.002 10.1007/s00500-019-04046-8 10.1016/j.eswa.2015.01.043 10.3233/jifs-179304 10.1371/journal.pone.0193789 10.1007/s10586-021-03235-1 10.1145/3391250 10.1016/j.physa.2019.123142 10.36548//jaicn.2020.3.006 10.2112/si93-150.1 10.1016/j.neunet.2009.11.016 10.1109/cc.2017.8107642 10.1007/s11063-019-10049-1 10.1007/s42488-019-00003-0 10.1371/journal.pone.0193223 10.1080/01969722.2012.659965 10.1080/00207543.2013.874602 10.1016/j.ins.2020.08.110 10.1016/j.neucom.2019.02.067 10.1007/s00521-020-05488-0 10.1080/00207540600810077 10.1016/j.resconrec.2019.104585 10.3233/jifs-179377 10.1007/jhep04(2021)138 10.1016/j.jtrangeo.2003.12.004 10.1109/tnnls.2013.2293637 10.1016/j.inffus.2021.05.011 10.1016/j.physa.2017.07.017 10.1080/00207543.2020.1841315 10.1142/s2196888820500050 10.1016/j.apm.2006.03.024 10.1109/access.2020.2964328 10.1155/2021/5241921 |
| ContentType | Journal Article |
| Copyright | Copyright © 2021 Binbin Liu. COPYRIGHT 2021 John Wiley & Sons, Inc. Copyright © 2021 Binbin Liu. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2021 Binbin Liu. 2021 |
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| SubjectTerms | Algorithms Analysis Artificial neural networks Cloud computing Commerce Data analysis Data mining Efficiency Electronic commerce Experiments Fuzzy logic Logistics Neural networks Neural Networks, Computer Optimization models R&D Research & development Route optimization Sawmills |
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| Title | Logistics Distribution Route Optimization Model Based on Recursive Fuzzy Neural Network Algorithm |
| URI | https://dx.doi.org/10.1155/2021/3338840 https://www.ncbi.nlm.nih.gov/pubmed/34777491 https://www.proquest.com/docview/2597344940 https://www.proquest.com/docview/2597807492 https://pubmed.ncbi.nlm.nih.gov/PMC8589477 https://downloads.hindawi.com/journals/cin/2021/3338840.pdf |
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