Abstract
The problem of the project management is performed with the optimization task under uncertainty and subject to real-world constraints. We use the probability theory and insufficiently proved methods, due to unavailable data indeed we need different methods for a best way to evaluate uncertainty. One of these approaches is based on the application of the fuzzy sets theory. Since its inception in 1965, the theory of fuzzy sets has advanced in a variety of ways and in many disciplines. Applications of this theory can be found, for example, in artificial intelligence, computer science, medicine, control engineering, decision theory, expert systems, logic, management science, operations research, pattern recognition, and robotics. This paper proposes a fuzzy decision making approach for project selection problem under uncertainty. An evaluation is provided as an illustration of the proposed approach. In the conclusion, we show how this method can help decision makers in the selection of appropriate project based on their profitability.
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References
Bard, J., Balachandra, R., Kaufmann, P.: An interactive approach to R&D project selection and termination. IEEE Trans. Eng. Manag. 35, 139–146 (1988)
Buckley, J.J.: The fuzzy mathematics of finance. Fuzzy Sets Syst. 21, 257–273 (1987)
Dimova, L., Sevastianov, D., Sevastianov, P.: Application of fuzzy sets theory, methods for the evaluation of investment efficiency parameters. Fuzzy Econ. Rev. 5, 34–48 (2000)
Eugene, R., Dey, P.K.: The role of environmental factors in industrial site selection activities: a case of limestone quarry expansion in Barbados. Impact Assess Proj. Appraisal. 23, 147–154 (2005)
Gillard, S., Price, J.: The competencies of effective project managers: a conceptual analysis. Int. J. Manag. 22, 48–53 (2005)
Hall, D.L., Nauda, A.: An interactive approach for selecting IR&D projects. IEEE Trans. Eng. Manag. 37, 126–131 (1990)
Kondratenko, Y.P., Encheva, S.B., Sidenko, I.V.: Synthesis of modular-structured systems for automatization of decision-making process in transport logistics. Int. J. Comput. 11, 336–344 (2012)
León, C., Machado, C.: Designing an expert knowledge-based Systemic Importance Index for financial institutions. Borradores de Economía. 669 (2011)
Loch, C.H., Kavadias, S.: Dynamic portfolio selection of NPD programs using marginal returns. Manage. Sci. 48, 1227–1241 (2002)
Markowitz, H.: Portfolio selection. J. Fin. 7(1), 77–91 (1952)
Reveiz, A., León, C.: Operational risk management using a fuzzy logic inference system. Borradores de Economia. 574, 9–24 (2009)
Stummer, C., Kiesling, E., Gutjahr, W.J.: A multicriteria decision support system for competence-driven project portfolio selection. Int. J. Inf. Technol. Decis. Mak. 8, 379–401 (2009)
Swathi, J.N., Rajen, B.B., Ilango, P., Khalid, M., Tripathy, B.K.: Induction of fuzzy decision trees and its refinement using gradient projected-neuro-fuzzy decision tree. Int. J. Adv. Intell. Paradig. 6, 346–369 (2014)
Wang, J., Hwang, W.L.: A fuzzy set approach for R&D portfolio selection using a real options valuation model. Omega 35, 247–257 (2007)
Xiang, G., Ying, W., Bei, Y.: A method for hesitant fuzzy multiple attribute decision making and its application to risk investment. J. Converg. Inf. Technol. 6, 282–287 (2011)
Yu, L., Shou, Y.W., Kin, K.L.: An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: the case of credit scoring. Eur. J. Oper. Res. 195, 942–959 (2009)
Zadeh, L.A.: Fuzzy Sets. Inf. Contr. 8, 338–353 (1965)
Zhou, X.Y., Li, D.: Continuous-time mean-variance portfolio selection: a stochastic LQ framework. Appl. Math. Optim. 42, 19–33 (2000)
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Ali, R., Mounir, G., Balas, V.E., Nissen, M. (2017). Fuzzy Evaluation Method for Project Profitability. In: Shakhovska, N. (eds) Advances in Intelligent Systems and Computing. Advances in Intelligent Systems and Computing, vol 512. Springer, Cham. https://doi.org/10.1007/978-3-319-45991-2_2
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DOI: https://doi.org/10.1007/978-3-319-45991-2_2
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