Abstract
This paper discusses a fuzzy chance-constrained project portfolio selection problem based on credibility theory. Risk of project portfolio is measured using conditional value at risk (CVaR) approach. The proposed model maximizes the expected fuzzy net present value (FNPV) subject to credibilistic chance constraint (CCC) of CVaR. We transform the chance-constrained model into deterministic model when the investment cost and return are characterized by triangular and trapezoidal fuzzy numbers. An improved genetic algorithm (GA) is designed to solve this problem. Two numerical examples with different types of membership function are also given to illustrate the modeling idea of the paper and to demonstrate the effectiveness of the proposed algorithm.
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Acknowledgments
This work is partly supported by Natural Science Foundation of China under grant No. 71240015, Natural Science Foundation of Guangdong Province under grant No. S2011010001337, Foundation for Distinguished Young Talents in Higher Education of Guangdong under grant 2012WYM_0116, the MOE Youth Foundation Project of Humanities and Social Sciences at Universities in China under grant 13YJC630123 and the Fundamental Research Funds for the Central Universities under grant 2012ZM0031.
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Li, L., Li, J., Qin, Q., Cheng, S. (2014). Fuzzy Chance-Constrained Project Portfolio Selection Model Based on Credibility Theory. In: Wen, Z., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54924-3_69
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DOI: https://doi.org/10.1007/978-3-642-54924-3_69
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