Scheduling Networks of Queues:  Asymptotically Optimal      Heuristics for Heavily Loaded Systems
{'Code': '07010000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'ECCS', 'LongName': 'Div Of Electrical, Commun & Cyber Sys'}}
{'SignBlockName': 'Kristen M. Biggar, N-BioS', 'PO_EMAI': None, 'PO_PHON': None}
This research considers the problem of stochastic control of queueing           networks.  There currently exist tractable models of stochastic                 queueing networks that allow multiple customer classes, but they permit         only very simple scheduling rules.  Thus, extant theory gives little            guidance as to how or how much system performance can be improved               through intelligent routing and sequencing of jobs.  This research will         examine a class of network scheduling problems and derive simple                heuristics that are asymptotically optimal under conditions of heavy            loading.  A simulation study will be undertaken to compare whatever             heuristics emerge from the theory against others appearing in the               literature.  In both the theoretical development and the simulation             study, special network structures characteristic of semiconductor               manufacturing systems will be emphasized.
{'FirstName': 'J.Michael', 'LastName': 'Harrison', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'J.Michael Harrison', 'EmailAddress': None, 'NSF_ID': '000213008', 'StartDate': '07/01/1985', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Stanford University', 'CityName': 'STANFORD', 'ZipCode': '943052004', 'PhoneNumber': '6507232300', 'StreetAddress': '450 JANE STANFORD WAY', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '16', 'CONGRESS_DISTRICT_ORG': 'CA16', 'ORG_UEI_NUM': 'HJD6G4D6TJY5', 'ORG_LGL_BUS_NAME': 'THE LELAND STANFORD JUNIOR UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}
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[{'Code': '1263', 'Text': 'PROBABILITY'}, {'Code': '1454', 'Text': 'SYSTEMS THEORY'}]
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1985&All=true', 'xml': '8503445.xml'}