Robust Optimization for Interactive Multiobjective Programming with Imprecise Information Applied to R&D Project Portfolio Selection

Farhad Hassanzaadeh, Hamid Nemati and Minghe Sun ()
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Minghe Sun: UTSA

Working Papers from College of Business, University of Texas at San Antonio

Abstract: A multiobjective binary integer programming model for R&D project portfolio selection with competing objectives is developed when problem coefficients in both objective functions and constraints are uncertain. Robust optimization is used in dealing with uncertainty while an interactive procedure is used in making tradeoffs among the multiple objectives. Robust nondominated solutions are generated by solving the linearized counterpart of the robust augmented weighted Tchebycheff programs. A decision maker’s most preferred solution is identified in the interactive robust weighted Tchebycheff procedure by progressively eliciting and incorporating the decision maker’s preference information into the solution process. An example is presented to illustrate the solution approach and performance. The developed approach can also be applied to general multiobjective mixed integer linear programming problems.

Keywords: Multiple objective programming; Robust optimization; Imprecise information; Portfolio selection; Interactive procedures. (search for similar items in EconPapers)
JEL-codes: C02 C61 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2013-07-03
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