Sensitivity Analysis and Real-Time Control of Discrete      Event Systems
{'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}
The two major objectives of this project are (a) to establish a                 general framework for estimating performance sensitivities of                   discrete event dynamic systems (DEDS) on-line, and (b) to                       integrate sensitivity information into optimization algorithms                  with real-time applicability.  The proposed research will pursue                some new directions in sample-path-based methodologies, using                   efficiently constructed "augmented systems."  Augmented systems                 are intended to capture the stochastic behavior of both a nominal               and perturbed DEDS, using only information directly observed.                   For a broad class of models and performance criteria, this                      framework is expected to provide sensitivity estimates with                     respect to parameters which may be real (e.g. job flows) or                     integer-valued (e.g. thresholds used in optimal dynamic                         policies).  Algorithms will be developed for dealing with                       fundamental DEDS control problems, such as dynamic routing,                     scheduling, and adaptive resource sharing.  Sensitivity analysis                will be integrated into static and dynamic distributed algorithms               for addressing performance optimization problems in the presence                of real-time constraints (deadlines).  The results of this                      project are expected to contribute to the growing effort towards                general methodologies for DEDS analysis.  They are also expected                to provide workable algorithms for on-line optimization of                      complex systems with no available analytical models.                                                                                                            The results from this work are very promising for dealing with                  control issues for such emerging technological environments as                  automated manufacturing, distributed processing systems, and                    computer networks. The PI is well qualified and the institution                 provides an excellent environment for the research.  A three-year               award is highly recommended.
{'FirstName': 'Christos', 'LastName': 'Cassandras', 'PI_MID_INIT': 'G', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Christos G Cassandras', 'EmailAddress': 'cgc@bu.edu', 'NSF_ID': '000112566', 'StartDate': '08/15/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Massachusetts Amherst', 'CityName': 'AMHERST', 'ZipCode': '010039252', 'PhoneNumber': '4135450698', 'StreetAddress': '101 COMMONWEALTH AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Massachusetts', 'StateCode': 'MA', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_ORG': 'MA02', 'ORG_UEI_NUM': 'VGJHK59NMPK9', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF MASSACHUSETTS', 'ORG_PRNT_UEI_NUM': 'VGJHK59NMPK9'}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
[{'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': '8801912.xml'}