Big Data Swarm Intelligence Optimization Algorithm Application in the Intelligent Management of an E-Commerce Logistics Warehouse

IF 0.7 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Cases on Information Technology Pub Date : 2023-10-26 DOI:10.4018/jcit.332809

Zhi Chen, Jie Liu, Ying Wang

{"title":"Big Data Swarm Intelligence Optimization Algorithm Application in the Intelligent Management of an E-Commerce Logistics Warehouse","authors":"Zhi Chen, Jie Liu, Ying Wang","doi":"10.4018/jcit.332809","DOIUrl":null,"url":null,"abstract":"A dynamic mutation probability formula is utilized to optimize the model. In order to solve the logistics warehouse path problem, the ant colony optimization algorithm, optimized by a genetic algorithm, is employed to construct a logistics warehouse path optimization model. This model effectively optimizes the logistics warehouse paths. Test results comparing the convergence and distribution of non-inferior solutions demonstrated that the proposed model outperforms others in terms of convergence and non-inferior solution distribution. In practical logistics warehouse optimization, applying the proposed model to optimize cargo locations can significantly enhance the effectiveness of the objective function. The optimization resulted in improvements for all four objective functions related to cargo location, with reduction rates of 10.38%, 30.88%, 51.78%, and 88.49%, respectively. For the optimization of logistics warehouse paths, the original distance was 47.6m, which was reduced to 27.8m after optimization. Consequently, the picking distance decreased by 41.60%.","PeriodicalId":43384,"journal":{"name":"Journal of Cases on Information Technology","volume":"454 3","pages":"0"},"PeriodicalIF":0.7000,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cases on Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/jcit.332809","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

A dynamic mutation probability formula is utilized to optimize the model. In order to solve the logistics warehouse path problem, the ant colony optimization algorithm, optimized by a genetic algorithm, is employed to construct a logistics warehouse path optimization model. This model effectively optimizes the logistics warehouse paths. Test results comparing the convergence and distribution of non-inferior solutions demonstrated that the proposed model outperforms others in terms of convergence and non-inferior solution distribution. In practical logistics warehouse optimization, applying the proposed model to optimize cargo locations can significantly enhance the effectiveness of the objective function. The optimization resulted in improvements for all four objective functions related to cargo location, with reduction rates of 10.38%, 30.88%, 51.78%, and 88.49%, respectively. For the optimization of logistics warehouse paths, the original distance was 47.6m, which was reduced to 27.8m after optimization. Consequently, the picking distance decreased by 41.60%.

查看原文本刊更多论文

大数据群智能优化算法在电子商务物流仓库智能管理中的应用

利用动态变异概率公式对模型进行优化。为了解决物流仓库路径问题，采用遗传算法优化的蚁群优化算法构建物流仓库路径优化模型。该模型有效地优化了物流仓库路径。比较非劣解的收敛性和分布的测试结果表明，该模型在收敛性和非劣解分布方面优于其他模型。在实际的物流仓库优化中，应用所提出的模型对货物位置进行优化，可以显著提高目标函数的有效性。优化后，与货位相关的4个目标函数均得到改善，降低率分别为10.38%、30.88%、51.78%和88.49%。对于物流仓库路径优化，原距离为47.6m，优化后缩短为27.8m。因此，采收距离减少了41.60%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Cases on Information Technology COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

2.60

自引率

0.00%

发文量

期刊介绍： JCIT documents comprehensive, real-life cases based on individual, organizational and societal experiences related to the utilization and management of information technology. Cases published in JCIT deal with a wide variety of organizations such as businesses, government organizations, educational institutions, libraries, non-profit organizations. Additionally, cases published in JCIT report not only successful utilization of IT applications, but also failures and mismanagement of IT resources and applications.