An application of artificial intelligence methods to scheduling parallel processors

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dc.contributor.authorSteffen, Mitchell S.en
dc.contributor.committeechairGreene, Timothy J.en
dc.contributor.committeememberSarin, Subhash C.en
dc.contributor.committeememberRoach, John W.en
dc.contributor.departmentIndustrial Engineering and Operations Researchen
dc.date.accessioned2014-03-14T21:37:39Zen
dc.date.adate2012-06-10en
dc.date.available2014-03-14T21:37:39Zen
dc.date.issued1985-08-05en
dc.date.rdate2012-06-10en
dc.date.sdate2012-06-10en
dc.description.abstractThis research investigated applying Artificial Intelligence (AI) method to develop a scheduling and sequencing system for parallel processors, subject to preference, sequencing, and buffer inventory constraints. Specifically, hierarchical planning and, constraint-directed search were used to develop prototype scheduling system for a case study problem. This research also investigated dividing the scheduling problem into sub-periods to allow parallel scheduling and efficient handling of time-dependent, constraints. The prototype system uses, problem-constraints to define sub-period boundaries, and determine which processors and jobs to include in the sub-period problems. It then solves the sub-period schedules in sequence. The prototype system was tested using operational data from the case study and compared to schedules created by the case study scheduler. The prototype system produced schedules very similar to the human scheduler, and relaxed constraints only slightly more than the scheduler in searching for solutions. The success of the prototype system demonstrated: 1) the effectiveness of hierarchical planning and constraint-directed search as methods for developing scheduling systems for parallel processors; 2) that constraint satisfaction, as opposed to solving an objective function, is a useful alternative method for modeling scheduling problems; and 3) dividing the scheduling problem into sub-period problems reduces the size of the search space- encountered in parallel scheduling while allowing fulfillment of time dependent constraints.en
dc.description.degreeMaster of Scienceen
dc.format.extentx, 170 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-06102012-040200en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-06102012-040200/en
dc.identifier.urihttp://hdl.handle.net/10919/43040en
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V855_1985.S747.pdfen
dc.relation.isformatofOCLC# 13037504en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.lccLD5655.V855 1985.S747en
dc.subject.lcshArtificial intelligenceen
dc.subject.lcshProduction scheduling -- Automationen
dc.titleAn application of artificial intelligence methods to scheduling parallel processorsen
dc.typeThesisen
dc.type.dcmitypeTexten
thesis.degree.disciplineIndustrial Engineering and Operations Researchen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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