{"title":"最佳门诊预约调度与紧急到达和一般服务时间","authors":"P. Koeleman, G. Koole","doi":"10.1080/19488300.2012.665154","DOIUrl":null,"url":null,"abstract":"Abstract In this paper we study the problem of deciding at what times to schedule non-emergency patients when there are emergency arrivals following a non-stationary Poisson process. The service times can have any given distribution. The objective function consists of a weighted sum of the waiting times, idle time and overtime. We prove that this objective function is multimodular, and then use a local search algorithm which in that case is guaranteed to find the optimal solution. Numerical examples show that this method gives considerable improvements over the standard even-spaced schedule, and that the schedules for different service time distributions can look quite different.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"2 1","pages":"14 - 30"},"PeriodicalIF":0.0000,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2012.665154","citationCount":"57","resultStr":"{\"title\":\"Optimal outpatient appointment scheduling with emergency arrivals and general service times\",\"authors\":\"P. Koeleman, G. Koole\",\"doi\":\"10.1080/19488300.2012.665154\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract In this paper we study the problem of deciding at what times to schedule non-emergency patients when there are emergency arrivals following a non-stationary Poisson process. The service times can have any given distribution. The objective function consists of a weighted sum of the waiting times, idle time and overtime. We prove that this objective function is multimodular, and then use a local search algorithm which in that case is guaranteed to find the optimal solution. Numerical examples show that this method gives considerable improvements over the standard even-spaced schedule, and that the schedules for different service time distributions can look quite different.\",\"PeriodicalId\":89563,\"journal\":{\"name\":\"IIE transactions on healthcare systems engineering\",\"volume\":\"2 1\",\"pages\":\"14 - 30\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/19488300.2012.665154\",\"citationCount\":\"57\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IIE transactions on healthcare systems engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/19488300.2012.665154\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IIE transactions on healthcare systems engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/19488300.2012.665154","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimal outpatient appointment scheduling with emergency arrivals and general service times
Abstract In this paper we study the problem of deciding at what times to schedule non-emergency patients when there are emergency arrivals following a non-stationary Poisson process. The service times can have any given distribution. The objective function consists of a weighted sum of the waiting times, idle time and overtime. We prove that this objective function is multimodular, and then use a local search algorithm which in that case is guaranteed to find the optimal solution. Numerical examples show that this method gives considerable improvements over the standard even-spaced schedule, and that the schedules for different service time distributions can look quite different.