Kim van den Houten, Léon Planken, Esteban Freydell, David M. J. Tax, Mathijs de Weerdt
{"title":"针对具有最大时滞的随机项目进度安排的主动和反应式约束编程","authors":"Kim van den Houten, Léon Planken, Esteban Freydell, David M. J. Tax, Mathijs de Weerdt","doi":"arxiv-2409.09107","DOIUrl":null,"url":null,"abstract":"This study investigates scheduling strategies for the stochastic\nresource-constrained project scheduling problem with maximal time lags\n(SRCPSP/max)). Recent advances in Constraint Programming (CP) and Temporal\nNetworks have reinvoked interest in evaluating the advantages and drawbacks of\nvarious proactive and reactive scheduling methods. First, we present a new,\nCP-based fully proactive method. Second, we show how a reactive approach can be\nconstructed using an online rescheduling procedure. A third contribution is\nbased on partial order schedules and uses Simple Temporal Networks with\nUncertainty (STNUs). Our statistical analysis shows that the STNU-based\nalgorithm performs best in terms of solution quality, while also showing good\nrelative offline and online computation time.","PeriodicalId":501479,"journal":{"name":"arXiv - CS - Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Proactive and Reactive Constraint Programming for Stochastic Project Scheduling with Maximal Time-Lags\",\"authors\":\"Kim van den Houten, Léon Planken, Esteban Freydell, David M. J. Tax, Mathijs de Weerdt\",\"doi\":\"arxiv-2409.09107\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study investigates scheduling strategies for the stochastic\\nresource-constrained project scheduling problem with maximal time lags\\n(SRCPSP/max)). Recent advances in Constraint Programming (CP) and Temporal\\nNetworks have reinvoked interest in evaluating the advantages and drawbacks of\\nvarious proactive and reactive scheduling methods. First, we present a new,\\nCP-based fully proactive method. Second, we show how a reactive approach can be\\nconstructed using an online rescheduling procedure. A third contribution is\\nbased on partial order schedules and uses Simple Temporal Networks with\\nUncertainty (STNUs). Our statistical analysis shows that the STNU-based\\nalgorithm performs best in terms of solution quality, while also showing good\\nrelative offline and online computation time.\",\"PeriodicalId\":501479,\"journal\":{\"name\":\"arXiv - CS - Artificial Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.09107\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.09107","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Proactive and Reactive Constraint Programming for Stochastic Project Scheduling with Maximal Time-Lags
This study investigates scheduling strategies for the stochastic
resource-constrained project scheduling problem with maximal time lags
(SRCPSP/max)). Recent advances in Constraint Programming (CP) and Temporal
Networks have reinvoked interest in evaluating the advantages and drawbacks of
various proactive and reactive scheduling methods. First, we present a new,
CP-based fully proactive method. Second, we show how a reactive approach can be
constructed using an online rescheduling procedure. A third contribution is
based on partial order schedules and uses Simple Temporal Networks with
Uncertainty (STNUs). Our statistical analysis shows that the STNU-based
algorithm performs best in terms of solution quality, while also showing good
relative offline and online computation time.