{"title":"应急服务职责分配的优化","authors":"Daniele Gilberti, G. Righini","doi":"10.1109/SOLI.2007.4383865","DOIUrl":null,"url":null,"abstract":"We introduce the problem of assigning alert duties to emergency service providers, namely civil protection teams and heart surgery wards in order to evenly distribute the workload among all providers and to optimize the level of service, measured by the intervention time. We compare two different exact optimization methods to solve this kind of problems, one based on general purpose integer linear programming solvers and the other based on Lagrangean relaxation and branch-and-bound. We also present a GRASP heuristic and we show how it can be used in combination with the general-purpose solver. Experimental results are presented on instances adapted from the O.R. Library and on instances taken from real applications.","PeriodicalId":154053,"journal":{"name":"2007 IEEE International Conference on Service Operations and Logistics, and Informatics","volume":"02 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Optimization of Duties Assignment in Emergency Services\",\"authors\":\"Daniele Gilberti, G. Righini\",\"doi\":\"10.1109/SOLI.2007.4383865\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce the problem of assigning alert duties to emergency service providers, namely civil protection teams and heart surgery wards in order to evenly distribute the workload among all providers and to optimize the level of service, measured by the intervention time. We compare two different exact optimization methods to solve this kind of problems, one based on general purpose integer linear programming solvers and the other based on Lagrangean relaxation and branch-and-bound. We also present a GRASP heuristic and we show how it can be used in combination with the general-purpose solver. Experimental results are presented on instances adapted from the O.R. Library and on instances taken from real applications.\",\"PeriodicalId\":154053,\"journal\":{\"name\":\"2007 IEEE International Conference on Service Operations and Logistics, and Informatics\",\"volume\":\"02 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE International Conference on Service Operations and Logistics, and Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SOLI.2007.4383865\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Conference on Service Operations and Logistics, and Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SOLI.2007.4383865","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimization of Duties Assignment in Emergency Services
We introduce the problem of assigning alert duties to emergency service providers, namely civil protection teams and heart surgery wards in order to evenly distribute the workload among all providers and to optimize the level of service, measured by the intervention time. We compare two different exact optimization methods to solve this kind of problems, one based on general purpose integer linear programming solvers and the other based on Lagrangean relaxation and branch-and-bound. We also present a GRASP heuristic and we show how it can be used in combination with the general-purpose solver. Experimental results are presented on instances adapted from the O.R. Library and on instances taken from real applications.
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