Decision Control Models in Operations Research
{'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 concentrates on stochastic optimization models.  The              research combines Markovian probability analysis, dynamic programming,          nonlinear programming, statistical curve fitting, and computer                  simulation analysis containing sophisticated statistical autoregressive         time series methods to investigate the mathematical characteristics of          stochastic optimization models.  The models relate to decision-making           about production and inventory levels, capacity levels, scheduling, and         resource allocation.  Advanced theoretical analyses are employed to             understand the mathematical properties of optimal policies under the            most general assumptions that yield analytic solutions.  In many cases          significant extensions of existing theory are required.  The properties         of optimal policies are exploited to derive analytic approximations             that are numerically accurate and more amenable to computation.  Issues         of computational simplicity and limited statistical information                 regarding parameter values are central to motivating the applying               statistical estimates as values for the model parameters.  In some              cases, the approximations can be explicitly modified to accommodate             this statistical context, while in other cases the research examines            the effects of using standard statistical estimates in place of actual          parameter values.
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{'Name': 'University of North Carolina at Chapel Hill', 'CityName': 'CHAPEL HILL', 'ZipCode': '275995023', 'PhoneNumber': '9199663411', 'StreetAddress': '104 AIRPORT DR STE 2200', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'North Carolina', 'StateCode': 'NC', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'NC04', 'ORG_UEI_NUM': 'D3LHU66KBLD5', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL', 'ORG_PRNT_UEI_NUM': 'N8DMK1K4C2K5'}
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{'Code': '1454', 'Text': 'SYSTEMS THEORY'}
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