{"title":"Logistics performance index estimating with artificial intelligence","authors":"Bilal Babayigit, Feyza Gürbüz, Berrin Denizhan","doi":"10.1504/ijstl.2023.129876","DOIUrl":null,"url":null,"abstract":"The World Bank has presented the logistics performance index (LPI) to measure and rank countries' international logistics performance. Based on six different components, the impact of each LPI component should be further investigated. In this paper, performance criteria are ranked using MGGP. This ranking approach is the first kind of study that enables countries to prioritise and adjust measures to evaluate their logistics performance better. MGGP is a recent promising approach among machine learning techniques, and it is capable of creating linear or nonlinear prediction models. LPI datasets consisting of 790 records collected between 2010-2018 are used to train and test the proposed MGGP approach. MGGP help address the logistics performance based on the relative importance of factors. The simulation results show the superiority of the MGGP approach predicting the LPI score. The prediction equation generated by MGGP can be helpful, for policymakers and researchers in logistics, in establishing logistics plans.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijstl.2023.129876","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 1
Abstract
The World Bank has presented the logistics performance index (LPI) to measure and rank countries' international logistics performance. Based on six different components, the impact of each LPI component should be further investigated. In this paper, performance criteria are ranked using MGGP. This ranking approach is the first kind of study that enables countries to prioritise and adjust measures to evaluate their logistics performance better. MGGP is a recent promising approach among machine learning techniques, and it is capable of creating linear or nonlinear prediction models. LPI datasets consisting of 790 records collected between 2010-2018 are used to train and test the proposed MGGP approach. MGGP help address the logistics performance based on the relative importance of factors. The simulation results show the superiority of the MGGP approach predicting the LPI score. The prediction equation generated by MGGP can be helpful, for policymakers and researchers in logistics, in establishing logistics plans.
期刊介绍：
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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