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Review of Empirical Studies in Multiobjective Mathematical Programming: Subject Reflection of Nonlinear Utility and Learning
Author(s) -
Olson David L.
Publication year - 1992
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1992.tb00374.x
Subject(s) - decision maker , computer science , management science , decision support system , goal programming , variety (cybernetics) , value (mathematics) , empirical research , operations research , mathematical optimization , decision analysis , nonlinear programming , nonlinear system , machine learning , artificial intelligence , mathematical economics , mathematics , economics , statistics , physics , quantum mechanics
Multiple objective programming provides a means of aiding decision makers facing complex decisions where trade‐offs among conflicting objectives must be reconciled. Interactive multiobjective programming provides a means for decision makers to learn what these trade‐offs involve, while the mathematical program generates solutions that seek improvement of the implied utility of the decision maker. A variety of multiobjective programming techniques have been presented in the multicriteria decision‐making literature. This study reviews published studies with human subjects where some of these techniques were applied. While all of the techniques have the ability to support decision makers under conditions of multiple objectives, a number of features in applying these systems have been tested by these studies. A general evolution of techniques is traced, starting with methods relying upon linear combinations of value, to more recent methods capable of reflecting nonlinear trade‐offs of value. Support of nonlinear utility and enhancing decision‐maker learning are considered.