ADVANCES IN MATHEMATICAL MODELING
Year:2016 | Volume:6 | Issue:1|Papers Count:6

In the changing economic conditions and volatility in financial environments in commercial is very important for predict financial performance, One of these is predict financial crisis and bankruptcy assessment. Data envelopment analysis (DEA) is a powerful tool available to managers that Benchmark your company's performance in their business activities. The conventional data envelopmentDEA models are to evaluate each DMU optimistically. DEA is evaluation model from the optimistic viewpoint. In fact it will be evaluated in optimistic state. However, other models have been introduced in the DEA to measure the efficiency with which the pessimistic viewpoint of their specific applications such as assessing the failures and bankruptcy. In this paper combine Rough set theory and a new model inDEA about bankruptcy and it measure the efficiency and bankruptcy with establishment of an information system and the use of indicators calculates bankruptcy and efficiency using DEARough concepts and Fuzzy Rough. The results of the model in determining bankruptcies reviewed the number of organization.

Direct estimators of parameters are not precise because of little surveys unit in small areas. Regarding the wide spread increase of demand for providing valid and accurate statistics for small areas, attempts have been made to present proper solutions for the problems. Small area estimation approaches provide the direct estimators with borrowing strength to increase their precision based on a model, especially about those estimators that are based on the linear mixed model including random area effects and using various auxiliary sources. Data associated with spatially contiguous small areas may be modeled via covariates, with error terms that are spatially dependent according to neighbor areas. In this paper we investigate small area estimation based on linear models with spatially correlated small area effects where the neighborhood structure is described by a contiguity matrix. Such models allow efficient use of spatial auxiliary information in small area estimation. Then estimation for small areas will be achieved for the amount of agronomy production in Fars province, according to the two common EBLUP and MBDE methods and two usual (non spatial) and spatial approaches based on the unit level model. Then the accuracy of them have been compared.

In this paper two distributions from the family of beta-G distributions with two distributions corresponding to the family Zgrafos-Balakrishnan-G, where G is a distribution from the family distributions of power series, are introduced. Then both families are compared by Monte Carlo simulation using goodness of fit test and rate risk function and two sets of real data. The results show that the family of distributions of beta-G is more suitable model for distribution of a lifetime.

Fuzzy regression represents the relation between variables. Fuzzy regression analysis is one of the most widely used statistical techniques. In this study, fuzzy regression is considered with the crisp input and the fuzzy output. A hybrid algorithm based on fuzzy weights and linear programming is designed for the fuzzy nonparametric regression model prediction that function form is assumed unknown and in cases low data. In proposed method, the objective function minimizes the spread of outputs. Finally, the performance of the suggested method is compared with linear programming (LP) and quadratic programming (QP) methods using the numerical examples. The results demonstrate that the proposed method has more accurate than the LP and QP methods. Also, it is verified in cases of low data.

In this paper, we consider a maximization problem related to a Laplacian equation with Dirichlet boundary conditions, where the admissible set is a rearrangement class of a non-negative function. When the domain of the equation is symmetric, under some suitable assumptions, we prove that the optimal solution of the maximization problem is symmetric and unique.

In most industrial applications, determining production quantities are one of the most key decision making. In this paper, an integer mathematical programming model for lot-sizing problem with considering set-up time, safety stock, shortage cost, and different production manners, is presented.The objective is to minimize summation of production, set up, holding, and shortage costs. To solve the model, a forward dynamic programming (DP) approach is presented and compared with classical backward DP method.Finally, different numerical illustrations with different dimension are generated. The statistical analysis on computational results showed that the proposed DP approach is more applicable than the classical DP in terms of computational time.