Fuzzy Mathematics in Finance

Buckley, James J.; Eslami, Esfandiar; Feuring, Thomas

doi:10.1007/978-3-7908-1795-9_4

Cited by 29 publications

(47 citation statements)

References 17 publications

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“…In these studies, either the periodic cash flow or the discount rate was specified as a fuzzy number. Buckley [11], Chiu and Park [12], and Kahraman et al [13] addressed problems in which both the periodic cash flow and the discount rate were expressed by fuzzy numbers, and developed various economic equivalence formulae for use in elementary financial calculations. However, the models developed in these various studies have only limited application in the economics decision-making arena since they consider only a single payment or at best, a few payments, when deriving their economic indexes.…”

Section: Jxmentioning

confidence: 99%

“…Following the rating of the fuzzy economic indexes by means of the fuzzy mathematics described above, the means and variances of these indexes can be calculated by equations (11) and (12), respectively. A preferred ranking of fuzzy numbers is then obtained by comparing the means and variances of the individual numbers.…”

Section: Fpbyl(c~)= Ln(1-[(rl-d3)+(r2-rl-+-da-d2)c¢][(ii+(i2-ii)c~)/(mentioning

confidence: 99%

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Fuzzy evaluation of cogeneration alternatives in a petrochemical industry

Sheen

2005

Computers & Mathematics with Applications

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This paper derives fuzzy net present value (NPV) and pay back year (PBY) models as decision indexes for cogeneration alternatives decision-making. The Mellin transform is employed to establish the means and variances of the fuzzy indexes in order to rank various cogeneration alternatives. It is noted that the mean and variance values depend only on the vertexes of the fuzzy index, and are independent of their height. The current paper verifies the performance of the proposed models by simulating two numerical examples and by considering a cogeneration program in a petrochemical industry. It is shown that the results of the proposed fuzzy economic models axe consistent with those of the conventional crisp models, and that the developed concepts are straightforward and easily implemented compared to the fuzzy ranking methods proposed in previous studies. Furthermore, the developed methods serve as readily implemented sensitivity analysis tools for use in the arena of uncertain decision-making. (~)

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Section: Jxmentioning

confidence: 99%

Section: Fpbyl(c~)= Ln(1-[(rl-d3)+(r2-rl-+-da-d2)c¢][(ii+(i2-ii)c~)/(mentioning

confidence: 99%

Fuzzy evaluation of cogeneration alternatives in a petrochemical industry

Sheen

2005

Computers & Mathematics with Applications

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“…Several publications discuss the methods of quantification of risk in investment projects [1,[5][6][7][8][9][10][11][12][13][14][15][16]. At present, methods with quantitative risk assessments are preferred.…”

Section: A Computer Simulation Applied To An Appraisal Of Investment mentioning

confidence: 99%

Fuzziness and randomness in investment project risk appraisal

Rębiasz

2007

Computers & Operations Research

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“…Consequently, a fuzzy present worth and fuzzy internal rate of return is found. Buckley [33] studied fuzzy present value and fuzzy future value by assuming a cash amount, interest rate, and period of time to be fuzzy. He also discussed methods of comparing fuzzy net cash flows in order to rank fuzzy investment alternatives from the best to the worst.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated fuzzy approach for the selection of manufacturing technologies

Chan

et al. 2005

Int J Adv Manuf Technol

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In this paper, an integrated decision support system is developed, which employs fuzzy techniques to assist decision-makers in choosing an optimal solution from alternative manufacturing options in an uncertain environment. The integrated approach incorporates different justification methods (e.g., strategic, economic, and analytic evaluations) for assessing tangible benefits, like cost, and intangible benefits, like quality, of different alternatives by a fuzzy multi-criteria decision-making method. As an illustrative example, selection of different advanced manufacturing technologies has been demonstrated by using the proposed methodology. The proposed concept will greatly reduce conflicts between tangible and intangible factors, and enhance the process of identification, interpretation, and diagnosis of the value of complex manufacturing and engineering systems

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Fuzzy Mathematics in Finance

Cited by 29 publications

References 17 publications

Fuzzy evaluation of cogeneration alternatives in a petrochemical industry

Fuzzy evaluation of cogeneration alternatives in a petrochemical industry

Fuzziness and randomness in investment project risk appraisal

An integrated fuzzy approach for the selection of manufacturing technologies

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