Fuzzy Cash Flow Analysis Using Present Worth Criterion

Chiu, Chih-Hui; Park, Chan S.

doi:10.1080/00137919408903117

Cited by 190 publications

(100 citation statements)

References 13 publications

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“…Two of the most recent and practical applications of nonprobabilistic uncertainty to economic analysis are given in [10], [11]. Choobineh and Behrens [11] call attention to the use of intervals and possibility theory in economic analysis.…”

Section: A Nonprobabilistic Methods In Cash Flow Analysismentioning

confidence: 99%

“…Their approach to modeling cash°ows as fuzzy intervals is similar to Ward's. Chiu and Park [10] use fuzzy numbers in cash°ow analysis and provide a good survey of the major methods for ranking mutually exclusive fuzzy projects. The cash°ows are modeled as triangular fuzzy numbers and the linear approximation to the product of two triangular fuzzy numbers is investigated.…”

Section: A Nonprobabilistic Methods In Cash Flow Analysismentioning

confidence: 99%

“…All ranking methods reported in the literature su®er from some pathological examples where the result is counterintuitive [7], [10]. The rankings of selected methods are given in Table II …”

Section: An Example Problemmentioning

confidence: 99%

“…In recent times the debate concerning the use of nonprobabilistic uncertainty, and speci¯cally fuzzy sets, has surfaced in the area of economic analysis [3], [8], [9], [10], [11], [19], [20], [42]. The replacement decisions made at each time period are based not only on the current cash°o ws but also projected future cash°ows of all possible assets [29].…”

Section: Replacement Analysismentioning

confidence: 99%

See 3 more Smart Citations

On replacement models via a fuzzy set theoretic framework

Esogbue

Hearnes

1998

IEEE Trans. Syst., Man, Cybern. C

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Abstract| Uncertainty is present in virtually all replacement decisions due to unknown future events, such as revenue streams, maintenance costs, and in°ation. Fuzzy sets provide a mathematical framework for explicitly incorporating imprecision into the decision making model, especially when the system involves human subjectivity. This paper illustrates the use of fuzzy sets and possibility theory to explicitly model uncertainty in replacement decisions via fuzzy variables and fuzzy numbers. In particular, a fuzzy set approach to economic life of an asset calculation as well as a¯-nite horizon single asset replacement problem with multiple challengers is discussed. Because the use of triangular fuzzy numbers provides a compromise between computational efciency and realistic modeling of the uncertainty, this discussion emphasizes fuzzy numbers. The algorithms used to determine the optimal replacement policy incorporate fuzzy arithmetic, dynamic programming with fuzzy rewards, the vertex method, and various ranking methods for fuzzy numbers. A brief history of replacement analysis, current conventional techniques, the basic concepts of fuzzy sets and possibility theory, and the advantages of the fuzzy generalization are also discussed.

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Section: A Nonprobabilistic Methods In Cash Flow Analysismentioning

confidence: 99%

Section: A Nonprobabilistic Methods In Cash Flow Analysismentioning

confidence: 99%

Section: An Example Problemmentioning

confidence: 99%

Section: Replacement Analysismentioning

confidence: 99%

See 2 more Smart Citations

On replacement models via a fuzzy set theoretic framework

Esogbue

Hearnes

1998

IEEE Trans. Syst., Man, Cybern. C

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“…In the literature we can find another approach to capital budgeting, that is, fuzzy capital budgeting. Several authors studied fuzzy set theory and its application in capital budgeting [3,5,7,11,13,14,21]. Some authors indicated certain problems to solve the capital budgeting problem with fuzzy numbers [3,5,6,21].…”

Section: Introductionmentioning

confidence: 99%

OFN Capital Budgeting Under Uncertainty and Risk

Chwastyk¹,

Pisz²

2017

Theory and Applications of Ordered Fuzzy Numbers

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The aim of this chapter is to propose a new approach to incorporating uncertainty into capital budgeting. The chapter presents methods that can be used by an investor when the decision maker wants to be able to make an investment decision where there are alternative investment projects. This kind of problem is undertaken under the conditions of uncertainty and risk using Ordered Fuzzy Numbers (OFN). The starting point is the concept of Ordered Fuzzy Numbers. The chapter illustrates the implementation of the proposed approach with an example where two alternative investment projects are analyzed. The authors present the capital budgeting problem using a numerical example. The described methods dedicated to investment project selection lay the foundations for a fuzzy decision-making system. We utilize computer software such as MATLAB to demonstrate how the proposed methods can be applied to assessing the profitability of alternative investment projects.

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Fuzzy sets approaches to statistical parametric and nonparametric tests

Kahraman

Bozdag

Ruan

et al. 2004

Int. J. Intell. Syst.

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The parametric tests often require that the population distributions be normal or approximately so. Statistical methods that do not require the knowledge of the population distribution or its parameters are called nonparametric tests. In this article, first we review some industrial applications of fuzzy parametric tests. Then we present some new algorithms for fuzzy nonparametric tests, namely a fuzzy sign test and a fuzzy Wilcoxon signed-ranks test. Later, we further give fuzzy parametric tests, fuzzy nonparametric tests, and their numerical applications, and also provide a comparison study on crisp and fuzzy nonparametric tests. When the data are vague, the result of the fuzzy nonparametric tests may be different from that of the crisp nonparametric tests.

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Fuzzy Cash Flow Analysis Using Present Worth Criterion

Cited by 190 publications

References 13 publications

On replacement models via a fuzzy set theoretic framework

On replacement models via a fuzzy set theoretic framework

OFN Capital Budgeting Under Uncertainty and Risk

Fuzzy sets approaches to statistical parametric and nonparametric tests

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