Abstract
Addressing uncertainty in Lorie-Savage and Weingartner capital rationing models has been considered in the literature with different approaches. Stochastic and robust approach to Weingartner capital rationing problem are examples of non-fuzzy approaches. In this chapter, we provide examples of fuzzy approach to Lorie-Savage problem, and illustrate the models with numerical examples. The solution of the generic models requires evolutionary algorithms; however for the models with triangular or trapezoidal fuzzy numbers, branch-and-bound method has been suggested to be sufficient.
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References
Ben-Tal, A., Nemirovski, A.: Robust solutions of linear programming problems contaminated with uncertain data. Mathematical Programming 88(3), 411–424 (2000)
Bertsimas, D., Sim, M.: The price of robustness. Operations Research 52(1), 35–53 (2004)
Buckley, J.J., Feuring, T.: Evolutionary algorithm solution to fuzzy problems: Fuzzy linear programming. Fuzzy Sets and Systems 109(1), 35–53 (2000)
Chen, S.-J., Hwang, C.-L.: Fuzzy multiple attribute decision making: Methods and applications. Springer, New York (1992)
Chiu, C.-Y., Park, C.S.: Fuzzy cash flow analysis using present worth criterion. The Engineering Economist 39(2), 113–138 (1994)
Chiu, C.-Y., Park, C.S.: Capital budgeting decisions with fuzzy projects. The Engineering Economist 43(2), 125–150 (1998)
Delgado, M., Verdegay, J.L., Vila, M.A.: A general model for fuzzy linear programming. Fuzzy Sets and Systems 29(1), 21–29 (1989)
Huang, X.: Chance-constrained programming models for capital budgeting with NPV as fuzzy parameters. Journal of Computational and Applied Mathematics 198(1), 149–159 (2007a)
Huang, X.: Optimal project selection with random fuzzy parameters. International Journal of Production Economics 106(2), 513–522 (2007b)
Huang, X.: Mean-variance model for fuzzy capital budgeting. Computers & Industrial Engineering (2008) doi: 10.1016/j.cie.2007.11.015
Kachani, S., Langella, J.: A robust optimization approach to capital rationing and capital budgeting. The Engineering Economis 50(3), 195–229 (2005)
Kira, D.S., Kusy, M.I.: A stochastic capital rationing model. The Journal of the Operational Research Society 41(9), 853–863 (1990)
Kira, D., Kusy, M., Rakita, I.: The effect of project risk on capital rationing under uncertainty. The Engineering Economis 45(1), 37–55 (2000)
Liu, B.: Random fuzzy dependent-chance programming and its hybrid intelligent algorithm. Information Sciences 141(3-4), 259–271 (2002)
Park, C.S., Sharp-Bette, G.P.: Advanced engineering economics. John Wiley & Sons, Inc., New York (1990)
Ramik, J.: Duality in fuzzy linear programming with possibility and necessity relations. Fuzzy Sets and System 157(10), 1283–1302 (2006)
Soyster, A.L.: Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations Research 21(5), 1154–1157 (1973)
Weingartner, H.M.: Mathematical programming and the analysis of capital budgeting problems. Markham Publishing Company, Chicago (1967)
Zheng, Y., Liu, B.: Fuzzy vehicle routing model with credibility measure and its hybrid intelligent algorithm. Applied Mathematics and Computation 176(2), 673–683 (2006)
Zimmermann, H.-J.: Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and System 1(1), 45–55 (1978)
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Bas, E., Kahraman, C. (2008). Fuzzy Capital Rationing Models. In: Kahraman, C. (eds) Fuzzy Engineering Economics with Applications. Studies in Fuzziness and Soft Computing, vol 233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70810-0_19
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DOI: https://doi.org/10.1007/978-3-540-70810-0_19
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