{"title":"一种改进的应急物流网络选址模型","authors":"Ming Liu","doi":"10.1109/LISS.2018.8593219","DOIUrl":null,"url":null,"abstract":"Emergency logistics network design is extremely important when responding to an unexpected epidemic pandemic. In this study, we propose an improved location-allocation model with an emphasis on maximizing the emergency service level (ESL). We formulate the problem to be a mixed-integer nonlinear programming model (MINLP) and develop an effective algorithm to solve the model. The numerical test shows that our model can provide tangible recommendations for controlling an unexpected epidemic.","PeriodicalId":338998,"journal":{"name":"2018 8th International Conference on Logistics, Informatics and Service Sciences (LISS)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Improved Location-allocation Model for Emergency Logistics Network Design\",\"authors\":\"Ming Liu\",\"doi\":\"10.1109/LISS.2018.8593219\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Emergency logistics network design is extremely important when responding to an unexpected epidemic pandemic. In this study, we propose an improved location-allocation model with an emphasis on maximizing the emergency service level (ESL). We formulate the problem to be a mixed-integer nonlinear programming model (MINLP) and develop an effective algorithm to solve the model. The numerical test shows that our model can provide tangible recommendations for controlling an unexpected epidemic.\",\"PeriodicalId\":338998,\"journal\":{\"name\":\"2018 8th International Conference on Logistics, Informatics and Service Sciences (LISS)\",\"volume\":\"57 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 8th International Conference on Logistics, Informatics and Service Sciences (LISS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/LISS.2018.8593219\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 8th International Conference on Logistics, Informatics and Service Sciences (LISS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LISS.2018.8593219","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Improved Location-allocation Model for Emergency Logistics Network Design
Emergency logistics network design is extremely important when responding to an unexpected epidemic pandemic. In this study, we propose an improved location-allocation model with an emphasis on maximizing the emergency service level (ESL). We formulate the problem to be a mixed-integer nonlinear programming model (MINLP) and develop an effective algorithm to solve the model. The numerical test shows that our model can provide tangible recommendations for controlling an unexpected epidemic.