Characterizing Trade-Offs Between Sensitivity and           Optimality:  The Tolerance Approach
{'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 primary focus of this research is on linear programming             sensitivity analysis.  The approach being proposed is called the                "tolerance approach" and the basic idea is to be able to tell the               decision maker that a given solution will be optimal as long as each            objective function coefficient is within a certain maximum tolerance            percentage of its nominal value.  The approach can exploit a priori             information on ranges of variability of the coefficients to yield larger        maximum tolerance percentages.  This research will generalize the               tolerance approach to characterize trade-offs between optimality and            sensitivity of a solution.  More specifically, the project will address         the following points:  to characterize trade-offs between optimality and        sensitivity of a solution and to develop efficient algorithms to                calculate such trade-offs for general linear programming problems; to           streamline these results to network problems; and to investigate                possible extensions of the results to nonlinear, integer, and multiple          objective programming problems.                                                         This research concerns the trade-offs between sensitivity and           optimality of a solution in linear programming.  Both theoretical               justification and efficient algorithms will be derived to characterize          and compute the trade-offs.  The results will be specialized for network        problems and possible extensions to multiple objective programming              problems will be investigated.  It is expected that the research will           yield valuable information which can be used by decision-makers in              implementing solutions of large optimization problems.
{'FirstName': 'Richard', 'LastName': 'Wendell', 'PI_MID_INIT': 'E', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Richard E Wendell', 'EmailAddress': None, 'NSF_ID': '000216141', 'StartDate': '05/15/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Pittsburgh', 'CityName': 'PITTSBURGH', 'ZipCode': '152600001', 'PhoneNumber': '4126247400', 'StreetAddress': '4200 FIFTH AVENUE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'StateCode': 'PA', 'CONGRESSDISTRICT': '18', 'CONGRESS_DISTRICT_ORG': 'PA18', 'ORG_UEI_NUM': 'MKAGLD59JRL1', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF PITTSBURGH - OF THE COMMONWEALTH SYSTEM OF HIGHER EDUCATION', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '1454', 'Text': 'SYSTEMS THEORY'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1986&All=true', 'xml': '8615302.xml'}