Vikas Agrawal, Aber Elsaleiby, Yue Zhang, P S Sundararaghavan, Andrew Casabianca
{"title":"极小最大值c手术调度中最大作业时间的百分位数。","authors":"Vikas Agrawal, Aber Elsaleiby, Yue Zhang, P S Sundararaghavan, Andrew Casabianca","doi":"10.1080/20476965.2019.1700763","DOIUrl":null,"url":null,"abstract":"<p><p>In this paper, we address the problem of finding an assignment of <i>n</i> surgeries to be performed in one of <i>m</i> parallel identical operating rooms (ORs), given each surgery has a stochastic duration with a known mean and standard deviation. The objective is to minimise the maximum of the <i>c<sup>th</sup></i> percentile of makespan of any OR. We formulate this problem as a nonlinear integer program, and small-sized instances are solved using the GAMS BONMIN solver. We develop a greedy heuristic and a genetic algorithm procedure for solving large-sized instances. Using real data from a major U.S. teaching hospital and benchmarking datasets from the literature, we report on the performance of the heuristics as compared to the GAMS BONMIN solver.</p>","PeriodicalId":44699,"journal":{"name":"Health Systems","volume":"10 2","pages":"118-130"},"PeriodicalIF":1.2000,"publicationDate":"2019-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/20476965.2019.1700763","citationCount":"5","resultStr":"{\"title\":\"Minimax <i>c</i> <sup>th</sup> percentile of makespan in surgical scheduling.\",\"authors\":\"Vikas Agrawal, Aber Elsaleiby, Yue Zhang, P S Sundararaghavan, Andrew Casabianca\",\"doi\":\"10.1080/20476965.2019.1700763\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In this paper, we address the problem of finding an assignment of <i>n</i> surgeries to be performed in one of <i>m</i> parallel identical operating rooms (ORs), given each surgery has a stochastic duration with a known mean and standard deviation. The objective is to minimise the maximum of the <i>c<sup>th</sup></i> percentile of makespan of any OR. We formulate this problem as a nonlinear integer program, and small-sized instances are solved using the GAMS BONMIN solver. We develop a greedy heuristic and a genetic algorithm procedure for solving large-sized instances. Using real data from a major U.S. teaching hospital and benchmarking datasets from the literature, we report on the performance of the heuristics as compared to the GAMS BONMIN solver.</p>\",\"PeriodicalId\":44699,\"journal\":{\"name\":\"Health Systems\",\"volume\":\"10 2\",\"pages\":\"118-130\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2019-12-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/20476965.2019.1700763\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/20476965.2019.1700763\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"HEALTH POLICY & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/20476965.2019.1700763","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"HEALTH POLICY & SERVICES","Score":null,"Total":0}
Minimax cth percentile of makespan in surgical scheduling.
In this paper, we address the problem of finding an assignment of n surgeries to be performed in one of m parallel identical operating rooms (ORs), given each surgery has a stochastic duration with a known mean and standard deviation. The objective is to minimise the maximum of the cth percentile of makespan of any OR. We formulate this problem as a nonlinear integer program, and small-sized instances are solved using the GAMS BONMIN solver. We develop a greedy heuristic and a genetic algorithm procedure for solving large-sized instances. Using real data from a major U.S. teaching hospital and benchmarking datasets from the literature, we report on the performance of the heuristics as compared to the GAMS BONMIN solver.