{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/33460"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/33460","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Approximating Deterministic Changes to Ph(t)/Ph(t)/1/c and Ph(t)/M(t)/s/c Queueing Models","abstract":"A deterministic change to a time-varying queueing model is described as either changing the number of entities, the queue capacity, or the number of servers in the system at selected times. We use a surrogate distribution for N(t), the number of entities in the system at time t, to approximate deterministic changes to the Ph(t)/Ph(t)/1/c and the Ph(t)/M(t)/s/c queueing models. 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We use a surrogate distribution for N(t), the number of entities in the system at time t, to approximate deterministic changes to the Ph(t)/Ph(t)/1/c and the Ph(t)/M(t)/s/c queueing models. We develop a solution technique to minimize the number of state probabilities to be approximated."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["Approximating Deterministic Changes to Ph(t)/Ph(t)/1/c and Ph(t)/M(t)/s/c Queueing Models"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Taaffe, Michael R."],"dc:contributor.committeemember":["Pasupathy, Raghu","Bish, Ebru K."],"dc:contributor.department":["Industrial and Systems Engineering"],"dc:creator":["Kulkarni, Aditya Umesh"],"dc:date.accessioned":["2014-03-14T20:39:32Z"],"dc:date.available":["2014-03-14T20:39:32Z","2012-06-15"],"dc:date.issued":["2012-05-25"],"dc:description.abstract":["A deterministic change to a time-varying queueing model is described as either changing the number of entities, the queue capacity, or the number of servers in the system at selected times. We use a surrogate distribution for N(t), the number of entities in the system at time t, to approximate deterministic changes to the Ph(t)/Ph(t)/1/c and the Ph(t)/M(t)/s/c queueing models. 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