{"title":"资源约束下项目调度的简单双ramp算法","authors":"C. Riley, C. Rego, Haitao Li","doi":"10.1145/1900008.1900097","DOIUrl":null,"url":null,"abstract":"A Relaxation Adaptive Memory Programming (RAMP) algorithm is developed to solve large-scale resource constrained project scheduling problems (RCPSP). The RAMP algorithm presented here takes advantage of a cross-parametric relaxation and extends a recent approach that casts the relaxed problem as a minimum cut problem. Computational results on a classical set of benchmark problems show that even a relatively simple implementation of the RAMP algorithm can find optimal or near-optimal solutions for a large set of those instances.","PeriodicalId":333104,"journal":{"name":"ACM SE '10","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A simple dual-RAMP algorithm for resource constraint project scheduling\",\"authors\":\"C. Riley, C. Rego, Haitao Li\",\"doi\":\"10.1145/1900008.1900097\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A Relaxation Adaptive Memory Programming (RAMP) algorithm is developed to solve large-scale resource constrained project scheduling problems (RCPSP). The RAMP algorithm presented here takes advantage of a cross-parametric relaxation and extends a recent approach that casts the relaxed problem as a minimum cut problem. Computational results on a classical set of benchmark problems show that even a relatively simple implementation of the RAMP algorithm can find optimal or near-optimal solutions for a large set of those instances.\",\"PeriodicalId\":333104,\"journal\":{\"name\":\"ACM SE '10\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM SE '10\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1900008.1900097\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SE '10","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1900008.1900097","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A simple dual-RAMP algorithm for resource constraint project scheduling
A Relaxation Adaptive Memory Programming (RAMP) algorithm is developed to solve large-scale resource constrained project scheduling problems (RCPSP). The RAMP algorithm presented here takes advantage of a cross-parametric relaxation and extends a recent approach that casts the relaxed problem as a minimum cut problem. Computational results on a classical set of benchmark problems show that even a relatively simple implementation of the RAMP algorithm can find optimal or near-optimal solutions for a large set of those instances.