2026-08-09T20:51:04Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:193 2024-03-06T11:06:12Z ddc:004 open_access

New Old Algorithms for Stochastic Scheduling Schulz, Andreas S. stochastic scheduling online algorithms competitive analysis approximation algorithms linear programming relaxations We consider the stochastic identical parallel machine scheduling problem and its online extension, when the objective is to minimize the expected total weighted completion time of a set of jobs that are released over time. We give randomized as well as deterministic online and offline algorithms that have the best known performance guarantees in either setting, online or offline and deterministic or randomized. Our analysis is based on a novel linear programming relaxation for stochastic scheduling problems that can be solved online. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Andreas S. Schulz 2005 Is Part Of Dagstuhl Seminar Proceedings, Volume 5031, Algorithms for Optimization with Incomplete Information (2005) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagSemProc.05031.18 urn:nbn:de:0030-drops-1931 https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05031.18 eng https://creativecommons.org/licenses/by/4.0/legalcode