Stochastic Convexity in Queueing Networks and Its           Application
{'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}
This proposal is jointly submitted by J.G. Shanthikumar of the                  University of California, Berkeley, and by D.D. Yao of Harvard                  University.  The focus of the proposed research project is on studying          convexity properties in queueing networks.  Such properties are often           indispensable in the optimal design and control of queueing networks,           which for the last three decades have been major tools in studying              complex stochastic systems such as computer systems, communication              networks, data-base systems, and manufacturing systems.                                                                                                         The PI's propose a new concept of stochastic convexity, and highlight           its applications in queueing network models of manufacturing systems.           This new concept captures the second-order (e.g., convexity or                  concavity) behavior of the stochastic processes in queueing networks            with respect to temporal and other parametric changes.  The queueing            network models that they focus on are major departures from classical           models; for instance, they allow general interarrival and service time          distributions, finite buffers and blocking.  Attention is also focused          on the (transient) analysis of the dynamic behavior of these networks.          Based on sample path analysis, they propose new approaches to                   establish stochastic convexity of certain key processes that underlie           these networks.                                                                                                                                                 Recently, stochastic optimization approaches that are based on Monte-           Carlo simulation have been developed to solve various optimal design            problems in queueing networks.  Stochastic convexity results provide            second-order optimality conditions for those approaches, and hence              significantly add to the theory and applications of queueing networks.
{'FirstName': 'J. George', 'LastName': 'Shanthikumar', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'J. George Shanthikumar', 'EmailAddress': 'jgshant@ieor.berkeley.edu', 'NSF_ID': '000180755', 'StartDate': '08/15/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of California-Berkeley', 'CityName': 'BERKELEY', 'ZipCode': '947101749', 'PhoneNumber': '5106433891', 'StreetAddress': '1608 4TH ST STE 201', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '12', 'CONGRESS_DISTRICT_ORG': 'CA12', 'ORG_UEI_NUM': 'GS3YEVSS12N6', 'ORG_LGL_BUS_NAME': 'REGENTS OF THE UNIVERSITY OF CALIFORNIA, THE', 'ORG_PRNT_UEI_NUM': None}
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[{'Code': '1454', 'Text': 'SYSTEMS THEORY'}, {'Code': '1518', 'Text': 'CONTROL, NETWORKS, & COMP INTE'}, {'Code': '5514', 'Text': 'OPERATIONS RESEARCH'}]
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8811234.xml'}