Variability and Affinity in Dynamic Decision Processes
{'Code': '07010000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'ECCS', 'LongName': 'Div Of Electrical, Commun & Cyber Sys'}}
{'SignBlockName': 'Radhakisan Baheti', 'PO_EMAI': None, 'PO_PHON': None}
Two directions of research are to be investigated for controlled                stochastic reward processes which model diverse phenomena in                    engineering.  The first direction augments the usual expected value             criteria with variability considerations.  Efficient methods are                sought to compute mean-variance tradeoffs, to compute the effects of a          "little" risk sensitivity, to characterize intertemporal variability,           and to extend such notions to sequential games.  General results would          be applied to structured models which arise in contexts such as                 inventory, production, maintenance, reservoirs, and storm water                 basins.  The second direction of research exploits preliminary results          for models with certain affine properties.  The properties                      significantly reduce the complexity of dynamic programming problems             and, in many cases, permit the dynamic model to be replaced with a              one-period model.  Efficient computational methods would be sought for          higher-order models which frequently arise in practice.  General                results would be combined with specially structured models such as in           queuing control, capacity expansion, inventory, multi-stage                     manufacturing, multi-reservoir river basins, aquaculture, and                   agricultural crop production combined with pest control.                                                                                                        This research concerns sequential decision processes.  In                       particular, two topics are being investigated:  (1) risk sensitivity            and (2) models which have certain affine properties allowing the                development of efficient computational procedures.  It is expected              that the results of this work will be applied to a wide range of                engineering problems such as multi-stage manufacturing and capacity             expansion.
{'FirstName': 'Matthew', 'LastName': 'Sobel', 'PI_MID_INIT': 'J', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Matthew J Sobel', 'EmailAddress': 'mjs13@po.cwru.edu', 'NSF_ID': '000163441', 'StartDate': '08/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
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[{'Code': '1454', 'Text': 'SYSTEMS THEORY'}, {'Code': '1473', 'Text': 'STRUCTURES I'}]
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