<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kandasamy, Nagarajan</style></author><author><style face="normal" font="default" size="100%">Hanak, David</style></author><author><style face="normal" font="default" size="100%">van Buskirk, Christopher</style></author><author><style face="normal" font="default" size="100%">Neema, Himanshu</style></author><author><style face="normal" font="default" size="100%">Karsai, Gabor</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Synthesis of Robust Task Schedules for Minimum Disruption Repair</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE International Conference on Systems, Man and Cybernetics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Dynamic scheduling</style></keyword><keyword><style  face="normal" font="default" size="100%">Integer linear programming</style></keyword><keyword><style  face="normal" font="default" size="100%">integer programming</style></keyword><keyword><style  face="normal" font="default" size="100%">Job shop scheduling</style></keyword><keyword><style  face="normal" font="default" size="100%">linear programming</style></keyword><keyword><style  face="normal" font="default" size="100%">manufacturing data processing</style></keyword><keyword><style  face="normal" font="default" size="100%">Manufacturing industries</style></keyword><keyword><style  face="normal" font="default" size="100%">minimum disruption repair</style></keyword><keyword><style  face="normal" font="default" size="100%">offline scheduling algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">Processor scheduling</style></keyword><keyword><style  face="normal" font="default" size="100%">robust task schedules synthesis</style></keyword><keyword><style  face="normal" font="default" size="100%">Robustness</style></keyword><keyword><style  face="normal" font="default" size="100%">scheduling</style></keyword><keyword><style  face="normal" font="default" size="100%">Scheduling algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">slack-based approach</style></keyword><keyword><style  face="normal" font="default" size="100%">Software systems</style></keyword><keyword><style  face="normal" font="default" size="100%">task graph</style></keyword><keyword><style  face="normal" font="default" size="100%">Timing</style></keyword><keyword><style  face="normal" font="default" size="100%">Uncertainty</style></keyword><keyword><style  face="normal" font="default" size="100%">unpredictable operating environment</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2004</style></date></pub-dates></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://archive.isis.vanderbilt.edu/sites/default/files/RobustTaskSchedules.pdf</style></url></related-urls></urls><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">5056-5061</style></pages><isbn><style face="normal" font="default" size="100%">0-7803-8566-7</style></isbn><abstract><style face="normal" font="default" size="100%">An off-line scheduling algorithm considers resource, precedence, and synchronization requirements of a task graph, and generates a schedule guaranteeing its timing requirements. This schedule must, however, be executed in a dynamic and unpredictable operating environment where resources may fail and tasks may execute longer than expected. To accommodate such execution uncertainties, this paper addresses the synthesis of robust task schedules using a slack-based approach and proposes a solution using integer linear programming (ILP). An ILP model, whose solution maximizes the temporal flexibility of the overall task schedule, is formulated. Two different ILP solvers are used to solve this model and their performance compared. For large task graphs, an efficient approximate method is presented and its performance evaluated.</style></abstract><accession-num><style face="normal" font="default" size="100%">8393579</style></accession-num></record></records></xml>