Asper School of Business

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Browsing Asper School of Business by Author "Appadoo, S. S."

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    Mehar Methods for Fuzzy Optimal Solution and Sensitivity Analysis of Fuzzy Linear Programming with Symmetric Trapezoidal Fuzzy Numbers
    (2014-4-22) Sidhu, Sukhpreet Kaur; Kumar, Amit; Appadoo, S. S.
    The drawbacks of the existing methods to obtain the fuzzy optimal solution of such linear programming problems, in which coefficients of the constraints are represented by real numbers and all the other parameters as well as variables are represented by symmetric trapezoidal fuzzy numbers, are pointed out, and to resolve these drawbacks, a new method (named as Mehar method) is proposed for the same linear programming problems. Also, with the help of proposed Mehar method, a new method, much easy as compared to the existing methods, is proposed to deal with the sensitivity analysis of the same type of linear programming problems.
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    Mellin’s Transform and Application to Some Time Series Models
    (2014-2-18) Appadoo, S. S.; Thavaneswaran, A.; Mandal, S.
    This paper uses the Mellin transform to establish the means, variances, skewness, and kurtosis of fuzzy numbers and applied them to the random coefficient autoregressive (RCA) time series models. We also give a close form expression to the moment generating function related to fuzzy numbers. It is shown that the results of the proposed time series models are consistent with those of the conventional time series models and that the developed concepts are straightforward and easily implemented.
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    Possibilistic Fuzzy Net Present Value Model and Application
    (2014-8-4) Appadoo, S. S.
    The cash flow values and the interest rate in the net present value (NPV) model are usually specified by either crisp numbers or random variables. In this paper, we first discuss some of the recent developments in possibility theory and find closed form expressions for fuzzy possibilistic net present value (FNPV). Then, following Carlsson and Fullér (2001), we discuss some of the possibilistic moments related to FNPV model along with an illustrative numerical example. We also give a unified approach to find higher order moments of FNPV by using the moment generating function introduced by Paseka et al. (2011).