New method of selecting efficient project portfolios in the presence of hybrid uncertainty

• Authors: RĘBIASZ Bogdan
• Languages of publication: EN

Abstracts

A new methods of selecting efficient project portfolios in the presence of hybrid uncertainty has been presented. Pareto optimal solutions have been defined by an algorithm for generating project portfolios. The method presented allows us to select efficient project portfolios taking into account statistical and economic dependencies between projects when some of the parameters used in the calculation of effectiveness can be expressed in the form of an interactive possibility distribution and some in the form of a probability distribution. The procedure for processing such hybrid data combines stochastic simulation with nonlinear programming. The interaction between data are modeled by correlation matrices and the interval regression. Economic dependences are taken into account by the equations balancing the production capacity of the company. The practical example presented indicates that an interaction between projects has a significant impact on the results of calculations.

Keywords

EN

portfolio selection; data processing; hybrid uncertainty; random fuzzy sets

• Publisher: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
• Journal: Operations Research and Decisions
• Year: 2016
• Volume: 26
• Issue: 4
• Pages: 65-90

Contributors

author

RĘBIASZ Bogdan

• AGH University of Science and Technology, ul. Gramatyka 10, 30-067 Kraków, Poland

References

• ACHARAYA DE P.K.D., SAHU K.C., A chance-constrained goal programming model for capital budgeting, J. Oper. Res. Soc., 1982, 33 (7), 635.
• APRIL J., GLOVER F., KELLY J.P., OPTFOLIO. A simulation optimization system for project portfolio planning, [in:] S. Chick. P. J. Sánchez, D. Ferrin, D.J. Morrice (Eds.), Proc. 2003 Winter Simulation Conference, 2, 301.
• BADRI M.A., DAVIS D., DAVIS D., A comprehensive 0–1 goal programming model for project selection, Int. J. Proj. Manage., 2001, 19 (4), 243.
• BAUDRIT C., DUBOIS D., GUYONET D., Joint propagation and exploitation of probabilistic and possibilistic information in risk assessment, IEEE Trans. Fuzzy Syst., 2006, 14 (5), 593.
• CHAN F.T.S., CHAN M.H., TANG N.K.H., Evaluation methodologies for technology selection, J. Mater. Proc. Techn., 2000, 107 (1–3), 330.
• COOPER A., FERSON S., GINZBURG L., Hybrid processing of stochastic and subjective uncertainty data, Risk Anal., 1996, 16 (6), 785.
• DEMPSTER A.P., Upper and Lower Probabilities Induced by a Multivalued Mapping, Ann. Math. Stat., 1967, 38 (2), 325.
• DICKINSON M.W., THOMTON A.C., GRAVES S., Technology portfolio management. Optimizing inter-dependent projects over multiple time period, IEE Trans. Eng. Manage., 2001, 48 (4), 518.
• DUBOIS D., GUYONNET D., Risk-informed decision-making in the presence of epistemic uncertainty, Int. J. Gen. Syst., 2011, 40 (2), 145.
• FERSON S., GINZBURG L.R., Different methods are needed to propagate ignorance and variability, Rel. Eng. Syst. Safety, 1996, 54 (2), 133.
• GUPTA C.P., A note on transformation of possibilistic information into probabilistic information for investment decisions, Fuzzy Sets Syst., 1993, 56 (2), 175.
• GUYONNET G., BOURGINE B., DUBOIS D., FARGIER H., CME B., CHILS P.J., Hybrid approach for ad-dressing uncertainty in risk assessment, J. Environ. Eng., 2003, 129 (1), 68.
• HASUIKE T., KATAGIRIB H., ISHII H., Portfolio selection problems with random fuzzy variable returns, Fuzzy Sets Syst., 2009, 160 (16), 2579.
• HLADÍK M., ČERNY M., Interval regression by tolerance analysis approach, Fuzzy Sets Syst., 2012, 193 (4), 85.
• HUANG X., Fuzzy chance-constrained portfolio selection, Appl. Math. Comp., 2006, 177 (2), 500.
• JAFFRAY J.-Y., FABRICE P., On the existence of subjective upper and lower probabilities, Math. Oper. Res., 1997, 22 (1), 165.
• JUN L., JIUPING X., Multi-objective portfolio selection model with fuzzy random returns and a compro-mise approach-based genetic algorithm, Inf. Sci., 2013, 220 (1), 507.
• KAKUZO I., BOADING L., Dependent-chance integer programming applied to capital budgeting, J. Oper. Res. Soc. Japan, 1999, 42 (2), 117.
• KUCHTA D., Fuzzy capital budgeting, Fuzzy Sets Syst., 2000, 111 (3), 367.
• LIU B., LIU Y.K., Expected value of fuzzy variable and fuzzy expected value models, IEE Trans. Fuzzy Syst., 2002, 10 (4), 445.
• LIU B., A survey of credibility theory, Fuzzy Opt. Dec. Making, 2006, 5 (4), 387.
• LIU B., IWAMURA K., Chance constrained programming with fuzzy parameters, Fuzzy Sets Syst., 1998, 94 (2), 227.
• NÄSLUND B., A model of capital budgeting under risk, J. Business, 1996, 39 (2), 89.
• PADBERG M., WILCZAK M.J., Optimal project selection when borrowing and lending rates differ, Math. Comp. Model., 1999, 29 (3), 63.
• RALESCU D.A., PURI M.L., The concept of normality for fuzzy random variable, Ann. Prob., 1985, 13 (4), 1371.
• REBIASZ B., Selection of efficient portfolios – probabilistic and fuzzy approach, comparative study, Comp. Ind. Eng., 2013, 64 (4), 1019.
• RĘBIASZ B., Hybrid correlated data in risk assessment, Oper. Res. Dec., 2015, 25 (1), 81.
• SANTHANAM R., KYPARISIS G.J., A decision model for interdependent information system project se-lection, Eur. J. Oper. Res., 1996, 89 (2), 380.
• SEITZ N.E., Capital Budgeting and Long-Term Financing Decisions, USA, South-Western College Publishing, 2004.
• SHAFER G., A Mathematical Theory of Evidence, Princeton University Press, Princeton 1976.
• SMETS P., Decision making in the TBM. The necessity of the pignistic transformation, Int. J. Appr. Reas., 2005, 38 (2), 133.
• SMETS P., Constructing the pignistic probability function in a context of uncertainty, [in:] M. Henrion, R.D. Shachter, L.N. Kanal, J.F. Lemmer (Eds.), Uncertainty in Artificial Intelligence, North Holland, Amsterdam 1990, 29–39.
• TAVANA M., KERAMATPOUR M., SANTOS-ARTEAGA F.J., GHORBANIANE E., A fuzzy hybrid project port-folio selection method using data envelopment analysis, TOPSIS and Integer Programming, Comp. Ind. Eng., 2015, 42 (22), 8432.
• ZULUAGA A., SEFAIR J., MEDAGLIA A., Model for the selection and scheduling of interdependent pro-jects, Proc. 2007 Systems and Information Engineering Design Symposium, Charlottesville 2007, 1.

Identifiers

DOI

10.5277/ord160405