Research on Adaptive Sampling and Stochastic Scheduling
{'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}
Two dynamic allocation problems are considered in this research.  The           first is the so-called "Multi-Armed Bandit Problem" (MABP), which has           applications to scheduling in job shops and in optimal replacement of           parts.  It has been shown that the general N dimensional MABP can be            reduced to N one-dimensional problems involving a single project, the           solution of which is the dynamic allocation index value for the current         state of the project.  The principal investigator proposes a new                approach for calculating the dynamic allocation index value and will            investigate how this approach can be applied to several problems in             sequential statistics and stochastic scheduling.  The second part of            this research deals with the relationship between closed (no arrivals)          and open (arrivals) stochastic scheduling problems.  The aim is to              understand the role played by the arrival process on the optimal                policies.
[{'FirstName': 'Michael', 'LastName': 'Katehakis', 'PI_MID_INIT': 'N', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Michael N Katehakis', 'EmailAddress': 'mnk@rutgers.edu', 'NSF_ID': '000119272', 'StartDate': '09/01/1985', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Edward', 'LastName': 'Beltrami', 'PI_MID_INIT': 'J', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Edward J Beltrami', 'EmailAddress': None, 'NSF_ID': '000182791', 'StartDate': '09/01/1985', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]
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{'Code': '1454', 'Text': 'SYSTEMS THEORY'}
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