Optimization methods have been widely applied in statistics. In mathematical programming, the coefficients of the models are always categorized as deterministic values. However uncertainty always exists in realistic problems. Therefore, interval-estimated optimization models may provide an alternative choice for considering the uncertainty into the optimization models. In this aspect, this paper concentrates, the lower and upper values of interval estimated linear fractional programming model (IELFPM) are obtained by using generalized confidence interval estimation method. An IELFPM is a LFP with interval form of the coefficients in the objective function and all requirements. The solution of the IELFPM is also analyzed.
Ananthalakshmi, S.; Vijayalakshmi, C.; and Ganesan, V.
Modern Approach for Designing and Solving Interval Estimated Linear Fractional Programming Models,
Applications and Applied Mathematics: An International Journal (AAM), Vol. 9,
2, Article 20.
Available at: https://digitalcommons.pvamu.edu/aam/vol9/iss2/20