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Soft Computing

, Volume 22, Issue 16, pp 5547–5559 | Cite as

Balancing strategic contributions and financial returns: a project portfolio selection model under uncertainty

  • Yuntao Guo
  • Lin Wang
  • Suike Li
  • Zhi Chen
  • Yin Cheng
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Abstract

This paper constructs a project portfolio selection model from the strategic perspective. Two goals are proposed for the portfolio to achieve, i.e., strategic contributions and financial returns. The uncertainties involved are addressed with fuzzy real options. Then, a modified multi-objective genetic algorithm is designed to determine the portfolios. Finally, a real case is provided to validate the model’s effectiveness. The results demonstrate that the proposed algorithm can optimize two objectives simultaneously and keep the plausible Pareto-optimal set which wins over the single-objective model solutions in achieving the shared value.

Keywords

Objective trade-offs Project portfolio selection Fuzzy real options Multi-objective genetic algorithm 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 71172123), Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2015JM7382), Social Science Foundation in Shaanxi Province of China (Program No. 2015R005), Soft Science Research Plan in Shaanxi Province of China (Program No. 2015KRM039).

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

References

  1. Abbassi M, Ashrafi M, Sharifi Tashnizi E (2014) Selecting balanced portfolios of R&D projects with interdependencies: a cross-entropy based methodology. Technovation 34(1):54–63CrossRefGoogle Scholar
  2. Alves MJ, Almeida M (2007) MOTGA: a multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem. Comput Oper Res 34(11):3458–3470MathSciNetCrossRefzbMATHGoogle Scholar
  3. Arratia MNM, López IF, Schaeffer SE, Cruz-Reyes L (2016) Static R&D project portfolio selection in public organizations. Decis Support Syst 84:53–63CrossRefGoogle Scholar
  4. Bednyagin D, Gnansounou E (2011) Real options valuation of fusion energy R&D programme. Energy Policy 39(1):116–130CrossRefGoogle Scholar
  5. Bhattacharyya R, Kumar P, Kar S (2011) Fuzzy R&D portfolio selection of interdependent projects. Comput Math Appl 62(10):3857–3870MathSciNetCrossRefzbMATHGoogle Scholar
  6. Birgisson I (2012) Project portfolio management in new product development organizations application of accepted PPM theories in practice (master’s thesis), Chalmers University of technologyGoogle Scholar
  7. Chiang IR, Nunez MA (2013) Strategic alignment and value maximization for IT project portfolios. Inf Technol Manag 14(2):143–157CrossRefGoogle Scholar
  8. Chiranjit Changdar, Mahapatra GS, Pal Rajat Kumar (2015) An improved genetic algorithm based approach to solve constrained knapsack problem in fuzzy environment. Expert Syst Appl 42(4):2276–2286CrossRefGoogle Scholar
  9. Cruz-Reyes L, Fernandez E, Sanchez P, Coello Coello CA, Gomez C (2017) Incorporation of implicit decision-maker preferences in multi-objective evolutionary optimization using a multi-criteria classification method. Appl Soft Comput J 50:48–57CrossRefGoogle Scholar
  10. De Oliveira LL, Freitas AA, Tinós R (2018) Multi-objective genetic algorithms in the study of the genetic code’s adaptability. Inf Sci 425:48–61MathSciNetCrossRefGoogle Scholar
  11. De Reyck B, Grushka-Cockayne Y, Lockett M, Calderini SR, Moura M, Sloper A (2005) The impact of project portfolio management on information technology projects. Int J Proj Manag 23(7):524–537CrossRefGoogle Scholar
  12. Hassanzadeh F, Collan M, Modarres M (2012) A practical approach to R&D portfolio selection using the fuzzy pay-off method. IEEE Trans Fuzzy Syst 20(4):615–622CrossRefGoogle Scholar
  13. Kalashnikov V, Benita F, López-Ramos F, Hernández-Luna A (2017) Bi-objective project portfolio selection in Lean Six Sigma. Int J Prod Econ 186:81–88CrossRefGoogle Scholar
  14. Karsak E (2006) A generalized fuzzy optimization framework for R&D project selection using real options valuation. In: Computational science and its applications-ICCSA, pp 918–927Google Scholar
  15. Khalili-Damghani K, Sadi-Nezhad S, Lotfi FH, Tavana M (2013) A hybrid fuzzy rule-based multi-criteria framework for sustainable project portfolio selection. Inf Sci 220:442–462CrossRefGoogle Scholar
  16. Li D, Gu H, Zhang L (2013) A hybrid genetic algorithm-fuzzy c -means approach for incomplete data clustering based on nearest-neighbor intervals. Soft Comput 17(10):1787–1796CrossRefGoogle Scholar
  17. Liesiö J, Salo A (2012) Scenario-based portfolio selection of investment projects with incomplete probability and utility information. Eur J Oper Res 217(1):162–172MathSciNetCrossRefzbMATHGoogle Scholar
  18. Lin FT (2008) Solving the knapsack problem with imprecise weight coefficients using genetic algorithms. Eur J Oper Res 185(1):133–145CrossRefzbMATHGoogle Scholar
  19. Mohagheghi V, Mousavi SM, Vahdani B (2015) A new optimization model for project portfolio selection under interval-valued fuzzy environment. Arab J Sci Eng 40(11):3351–3361MathSciNetCrossRefzbMATHGoogle Scholar
  20. Nassif LN, Filho JCS, Nogueira JM (2013) Project portfolio selection in public administration using fuzzy logic. Proc Soc Behav Sci 74:41–50CrossRefGoogle Scholar
  21. Perez-Escobedo JL, Azzaro-Pantel C, Pibouleau L (2012) Multiobjective strategies for New Product Development in the pharmaceutical industry. Comput Chem Eng 37:278–296CrossRefGoogle Scholar
  22. Piroozfard H, Wong KY, Wong WP (2018) Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm. Resour Conserv Recycl 128:267–283CrossRefGoogle Scholar
  23. Relich M, Pawlewski P (2017) A fuzzy weighted average approach for selecting portfolio of new product development projects. Neurocomputing 231:19–27CrossRefGoogle Scholar
  24. Shu L, Jiang P, Zhou Q, Shao X, Hu J, Meng X (2018) An on-line variable fidelity metamodel assisted multi-objective genetic algorithm for engineering design optimization. Appl Soft Comput 66:438–448CrossRefGoogle Scholar
  25. Tolga AÇ (2012) A real options approach for software development projects using fuzzy electre. J Mult Valued Logic Soft Comput 18:541–560Google Scholar
  26. Wang J, Wang J, Hwang W (2007) A fuzzy set approach for R & D portfolio selection using a real options valuation model A fuzzy set approach for R & D portfolio selection using a real options valuation model. Omega 35:247–257CrossRefGoogle Scholar
  27. Wang Q, Kilgour DM, Hipel KW (2011) Fuzzy real options for risky project evaluation using least squares Monte-Carlo simulation. IEEE Syst J 5(3):385–395Google Scholar
  28. Xu XF, Zhang W, Li N, Xu HL (2015) A bi-level programming model of resource matching for collaborative logistics network in supply uncertainty environment. J Frankl Inst 352:3873–3884MathSciNetCrossRefGoogle Scholar
  29. Xu XF, Hao J, Deng YR, Wang Y (2017) Design optimization of resource combination for collaborative logistics network under uncertainty. Appl Soft Comput 560(7):684–691CrossRefGoogle Scholar
  30. Yan S, Ji X (2017) Portfolio selection model of oil projects under uncertain environment. Soft Comput.  https://doi.org/10.1007/s00500-017-2619-2
  31. Yassine AA, Mostafa O, Browning TR (2017) Scheduling multiple, resource-constrained, iterative, product development projects with genetic algorithms. Comput Ind Eng 107:39–56CrossRefGoogle Scholar
  32. You CJ, Lee CKM, Chen SL, Jiao RJ (2012) A real option theoretic fuzzy evaluation model for enterprise resource planning investment. J Eng Technol Manag JET-M 29(1):47–61CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yuntao Guo
    • 1
  • Lin Wang
    • 1
  • Suike Li
    • 1
  • Zhi Chen
    • 1
  • Yin Cheng
    • 1
  1. 1.Management SchoolNorthwestern Polytechnical UniversityXi’anChina

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