Fuzzy net present value for engineering analysis


Mehdi Nosratpour, Ali Nazeri and Hadi Meftahi


Cash flow analysis is one of the most popular methods for investigating the outcome of an economical project. The costs and benefits of a construction project are often involved with uncertainty and it is not possible to find a precise value for a particular project. In this paper, we present a simple method to calculate the net present value of a cash flow when both costs and benefits are given as triangular numbers. The proposed model of this paper uses Delphi method to figure out the fair values of all costs and revenues and then using fizzy programming techniques, it calculates the fuzzy net present value. The implementation of the proposed model is demonstrated using a simple example.


DOI: j.msl.2012.06.002

Keywords: Net present value ,NPV ,Fuzzy number ,Fuzzy NPV

How to cite this paper:

Nosratpour, M., Nazeri, A & Meftahi, H. (2012). Fuzzy net present value for engineering analysis.Management Science Letters, 2(6), 2153-2158.


References

Ho, S.H., & Liao, S.H. (2011). A fuzzy real option approach for investment project valuation. Expert Systems with Applications, 38(12), 15296-15302

Huang, X. (2007). Chance-constrained programming models for capital budgeting with NPV as fuzzy parameters. Journal of Computational and Applied Mathematics, 198(1), 149-159.

Huang, X. (2008). Mean-variance model for fuzzy capital budgeting. Computers & Industrial Engineering, 55(1), 34-47.

Kahraman, C., Tolga, E., & Ulukan, Z. (2000). Justification of manufacturing technologies using fuzzy benefit/cost ratio analysis. International Journal of Production Economics, 66(1), 45-52.

Kahraman, C., Ruan, D., & Tolga, E. (2002). Capital budgeting techniques using discounted fuzzy versus probabilistic cash flows. Information Sciences, 142(1-4), 57-76.

Liao, S.H., & Ho, S.H. (2010). Investment project valuation based on a fuzzy binomial approach. Information Sciences, 180(11), 2124-2133.

Remer, D.S., & Nieto, A.P. (1995). A compendium and comparison of 25 project evaluation techniques. Part 1: Net present value and rate of return methods. International Journal of Production Economics, 42(1), 79-96.

Shahsavar, M., Akhavan Niaki, S.T., & Najafi, A.A. (2010). An efficient genetic algorithm to maximize net present value of project payments under inflation and bonus–penalty policy in resource investment problem. Advances in Engineering Software, 41(7-8), 1023-1030.

Sheen, J.N. (2005). Fuzzy evaluation of cogeneration alternatives in a petrochemical industry. Computers & Mathematics with Applications, 49(5-6), 741-755

Sobel, M.J., Szmerekovsky, J.G., & Tilson, V. (2009). Scheduling projects with stochastic activity duration to maximize expected net present value. European Journal of Operational Research, 198(3), 697-705.

Tsao, C.T. (2012). Fuzzy net present values for capital investments in an uncertain environment. Computers & Operations Research, 39(8), 1885-1892.

Ustundag, A., Kılınç, M.S., & Cevikcan, E. (2010). Fuzzy rule-based system for the economic analysis of RFID investments. Expert Systems with Applications, 37(7), 5300-5306

Zimmermann, H.J (1996). Fuzzy Set Theory. 3rd ed., Kluwer academic publisher.

Zadeh, A. (1975a). The concept of a linguistic variable and its application to approximate reasoning, part 1. Information Sciences, 8(3), 199–249.

Zadeh, A. (1975b). The concept of a linguistic variable and its application to approximate reasoning, part 2. Information Sciences, 8(4), 301–357.

Zadeh, A. (1975c). The concept of a linguistic variable and its application to approximate reasoning, part 3. Information Sciences, 9(1), 43–58.