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

Author

Listed:
  • Farhad Hassanzadeh
  • Hamid Nemati
  • Minghe Sun

    (UTSA)

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.

Suggested Citation

  • Farhad Hassanzadeh & Hamid Nemati & Minghe Sun, 2013. "Robust Optimization for Interactive Multiobjective Programming with Imprecise Information Applied to R&D Project Portfolio Selection," Working Papers 0194mss, College of Business, University of Texas at San Antonio.
    • Handle: RePEc:tsa:wpaper:0194mss
    as

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    File URL: http://interim.business.utsa.edu/wps/mss/0011MSS-061-2013.pdf
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    References listed on IDEAS

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    1. Sun, Minghe, 2005. "Some issues in measuring and reporting solution quality of interactive multiple objective programming procedures," European Journal of Operational Research, Elsevier, vol. 162(2), pages 468-483, April.
    2. Medaglia, Andres L. & Graves, Samuel B. & Ringuest, Jeffrey L., 2007. "A multiobjective evolutionary approach for linearly constrained project selection under uncertainty," European Journal of Operational Research, Elsevier, vol. 179(3), pages 869-894, June.
    3. A. L. Soyster, 1973. "Technical Note—Convex Programming with Set-Inclusive Constraints and Applications to Inexact Linear Programming," Operations Research, INFORMS, vol. 21(5), pages 1154-1157, October.
    4. Ralph E. Steuer & Minghe Sun, 1995. "The Parameter Space Investigation Method of Multiple Objective Nonlinear Programming: A Computational Investigation," Operations Research, INFORMS, vol. 43(4), pages 641-648, August.
    5. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    6. Schniederjans, Marc J. & Santhanam, Radhika, 1993. "A multi-objective constrained resource information system project selection method," European Journal of Operational Research, Elsevier, vol. 70(2), pages 244-253, October.
    7. Medaglia, Andres L. & Hueth, Darrell & Mendieta, Juan Carlos & Sefair, Jorge A., 2008. "A multiobjective model for the selection and timing of public enterprise projects," Socio-Economic Planning Sciences, Elsevier, vol. 42(1), pages 31-45, March.
    8. Abdelaziz, Fouad Ben & Aouni, Belaid & Fayedh, Rimeh El, 2007. "Multi-objective stochastic programming for portfolio selection," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1811-1823, March.
    9. Gabriel, Steven A. & Kumar, Satheesh & Ordonez, Javier & Nasserian, Amirali, 2006. "A multiobjective optimization model for project selection with probabilistic considerations," Socio-Economic Planning Sciences, Elsevier, vol. 40(4), pages 297-313, December.
    10. Zanakis, Stelios H. & Mandakovic, Tomislav & Gupta, Sushil K. & Sahay, Sundeep & Hong, Sungwan, 1995. "A review of program evaluation and fund allocation methods within the service and government sectors," Socio-Economic Planning Sciences, Elsevier, vol. 29(1), pages 59-79, March.
    11. Rania Azmi & Mehrdad Tamiz, 2010. "A Review of Goal Programming for Portfolio Selection," Lecture Notes in Economics and Mathematical Systems, in: Dylan Jones & Mehrdad Tamiz & Jana Ries (ed.), New Developments in Multiple Objective and Goal Programming, pages 15-33, Springer.
    12. Klapka, Jindrich & Pinos, Petr, 2002. "Decision support system for multicriterial R&D and information systems projects selection," European Journal of Operational Research, Elsevier, vol. 140(2), pages 434-446, July.
    13. Doerner, K.F. & Gutjahr, W.J. & Hartl, R.F. & Strauss, C. & Stummer, C., 2006. "Pareto ant colony optimization with ILP preprocessing in multiobjective project portfolio selection," European Journal of Operational Research, Elsevier, vol. 171(3), pages 830-841, June.
    14. Shing, Chue & Nagasawa, Hiroyuki, 1999. "Interactive decision system in stochastic multiobjective portfolio selection," International Journal of Production Economics, Elsevier, vol. 60(1), pages 187-193, April.
    15. F Ghasemzadeh & N Archer & P Iyogun, 1999. "A zero-one model for project portfolio selection and scheduling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(7), pages 745-755, July.
    16. Lean Yu & Shouyang Wang & Fenghua Wen & Kin Lai, 2012. "Genetic algorithm-based multi-criteria project portfolio selection," Annals of Operations Research, Springer, vol. 197(1), pages 71-86, August.
    17. Karl Doerner & Walter Gutjahr & Richard Hartl & Christine Strauss & Christian Stummer, 2004. "Pareto Ant Colony Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection," Annals of Operations Research, Springer, vol. 131(1), pages 79-99, October.
    18. ,, 2000. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 16(2), pages 287-299, April.
    19. Ringuest, Jeffrey L. & Graves, Samuel B., 2000. "A sampling-based method for generating nondominated solutions in stochastic MOMP problems," European Journal of Operational Research, Elsevier, vol. 126(3), pages 651-661, November.
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    More about this item

    Keywords

    Multiple objective programming; Robust optimization; Imprecise information; Portfolio selection; Interactive procedures.;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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