COVID-19 Vaccines distribution routes with bioinspired metaheuristic algorithms: Resoluteness

IF 1.3 4区工程技术 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Latin America Transactions Pub Date : 2024-03-20 DOI:10.1109/TLA.2024.10534309

Leonardo Daniel Estrada Moreno;Rosa María Valdovinos Rosas;Lourdes Loza Hernandez;Roberto Alejo

{"title":"COVID-19 Vaccines distribution routes with bioinspired metaheuristic algorithms: Resoluteness","authors":"Leonardo Daniel Estrada Moreno;Rosa María Valdovinos Rosas;Lourdes Loza Hernandez;Roberto Alejo","doi":"10.1109/TLA.2024.10534309","DOIUrl":null,"url":null,"abstract":"The global emergency of COVID-19 caused by the SARS-CoV-2 virus at the end of 2019, was without a doubt a critical and historical point for society in general; for instance, the effective development of vaccines, as well as the efficient distribution of them; They were an unprecedented challenge to slow down the spread or mitigate its impact on societies around the world. This article specifies three bio-inspired metaheuristic algorithms (genetic algorithm, particle swarm optimization algorithm, and artificial bee colony algorithm) that were used in the context of the capacitated vehicle routing problem to generate vaccine distribution routes, specifically, COVID-19 vaccine for over 18 years old the first and the second doses applications in Mexico, particularly in the State of Mexico. The quality of the solutions obtained by these algorithms is compared, as a result of the performance of the particle swarm optimization (PSO) algorithm being superior in solution quality.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10534309","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Latin America Transactions","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10534309/","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

The global emergency of COVID-19 caused by the SARS-CoV-2 virus at the end of 2019, was without a doubt a critical and historical point for society in general; for instance, the effective development of vaccines, as well as the efficient distribution of them; They were an unprecedented challenge to slow down the spread or mitigate its impact on societies around the world. This article specifies three bio-inspired metaheuristic algorithms (genetic algorithm, particle swarm optimization algorithm, and artificial bee colony algorithm) that were used in the context of the capacitated vehicle routing problem to generate vaccine distribution routes, specifically, COVID-19 vaccine for over 18 years old the first and the second doses applications in Mexico, particularly in the State of Mexico. The quality of the solutions obtained by these algorithms is compared, as a result of the performance of the particle swarm optimization (PSO) algorithm being superior in solution quality.

查看原文本刊更多论文

COVID-19 采用生物启发元启发式算法的疫苗配送路线：稳定性

2019年底，由SARS-CoV-2病毒引发的COVID-19全球紧急事件无疑是整个社会的一个关键和历史性节点；例如，疫苗的有效开发和高效分配；它们是减缓其传播或减轻其对全球社会影响的前所未有的挑战。本文介绍了三种生物启发元启发式算法（遗传算法、粒子群优化算法和人工蜂群算法），这些算法被用于容车路由问题，以生成疫苗配送路线，特别是用于墨西哥，尤其是墨西哥州 18 岁以上人群第一剂和第二剂 COVID-19 疫苗的配送路线。对这些算法所获得的解决方案的质量进行了比较，结果发现粒子群优化（PSO）算法的性能在解决方案质量方面更胜一筹。

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

IEEE Latin America Transactions COMPUTER SCIENCE, INFORMATION SYSTEMS-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

3.50

自引率

7.70%

发文量

192

审稿时长

3-8 weeks

期刊介绍： IEEE Latin America Transactions (IEEE LATAM) is an interdisciplinary journal focused on the dissemination of original and quality research papers / review articles in Spanish and Portuguese of emerging topics in three main areas: Computing, Electric Energy and Electronics. Some of the sub-areas of the journal are, but not limited to: Automatic control, communications, instrumentation, artificial intelligence, power and industrial electronics, fault diagnosis and detection, transportation electrification, internet of things, electrical machines, circuits and systems, biomedicine and biomedical / haptic applications, secure communications, robotics, sensors and actuators, computer networks, smart grids, among others.