N. Bindu Madhavi, G. Kannan, K. Vinayagam, M. Jayaprakash
{"title":"Supply Chain Management Using Bee Swarm Optimisation to Improve the Logistics in E- Commerce Era","authors":"N. Bindu Madhavi, G. Kannan, K. Vinayagam, M. Jayaprakash","doi":"10.1109/ICDT57929.2023.10150921","DOIUrl":null,"url":null,"abstract":"The traditional supply chain management and logistics aims at delivering the shipments to user end without delay. The research provides directions for the supply chain management to improve the chain of logistics for the ecommerce sites using nature inspired bee swarm optimization. The utilization of the nature inspired model improves the decision-making ability of the supply chain logistics using various input parameters. The simulation is tested with the development of supply and track model in python tool that uses bee swarm intelligence to track the logistics chain and thereby mitigating the delay in delivering the shipments to the user end. The results show that the proposed intelligence model achieves a higher tracking efficiency than the existing SOTA.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Disruptive Technologies (ICDT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDT57929.2023.10150921","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The traditional supply chain management and logistics aims at delivering the shipments to user end without delay. The research provides directions for the supply chain management to improve the chain of logistics for the ecommerce sites using nature inspired bee swarm optimization. The utilization of the nature inspired model improves the decision-making ability of the supply chain logistics using various input parameters. The simulation is tested with the development of supply and track model in python tool that uses bee swarm intelligence to track the logistics chain and thereby mitigating the delay in delivering the shipments to the user end. The results show that the proposed intelligence model achieves a higher tracking efficiency than the existing SOTA.