Data Envelopment Analysis (DEA) cannot provide adequate discrimination among efficient decision making units (DMUs). To discriminate these efficient DMUs is an interesting research subject. The purpose of this paper is to develop the mix integer linear model which was proposed by Foroughi (Foroughi A.A. A new Mixed integer linear model for selecting the best decision making units in data envelopment analysis. Computers & Industrial Engineering 60 (2011) 550-554) to present new alternative mix integer programming DEA (MIP-DEA) models which can be used to improve discrimination power of DEA and select the most BCC- efficient decision making unit (DMU). We will demonstrate that proposed model is able to select DMU throughout the real data sets.

IntroductionFloods, droughts, water scarcity, water contaminants, and optimal use of water resources including reservoirs and aquifers are some of the many water problems present today. These will be even more noticeable in the future. The optimal operation of reservoirs is one of the best ways in water resources management to deal with such problems and other unwanted temporal and spatial distributions of water. Optimization techniques have become increasingly important in the management and operation of complex reservoir systems over the past three decades. Some researches have provided an extensive literature review and evaluation of various optimization methods and their corresponding models. Each optimization method has its advantages and disadvantages that make them suitable for some problems. Selection of each method depends on the characteristics of the system being considered, data availability, optimization objectives, and the problem constraints.

Most of managerial problems, as financial and specially financing problems, naturally consist of multiple-conflicting objectives and goal programming (GP) regard as an efficient and powerful technique for this kind of problems. In this paper, first a simple single period single objective model of external financing is provided. In order to assist financial decision maker (DM)"s in making optimal financing decisions, a multiperiod-multiobjective Mixed integer GP model is introduced. This model allows DM to consider several and even conflicting objectives over time and the solution of the proposed model answers to the most important financing questions such as when, how much, and from which financing source(s) should a firm finance its required funds? Since financing planning without considering future investment opportunities is just oversimplifying the reality, the paper also explains how these opportunities can be entered and evaluated in the model and how they may effect on the optimal financing strategy.

The measure of Reactive power optimization of main power network mainly considers on-load tap changer, the optimal capacity of the capacitor, the voltage of generator under the steady load. In This paper, a multi-objective optimization methodology to the Optimal Reactive Power Flow (ORPF) problem is proposed in which the e-constrained approach is implemented for the Multi-objective Mathematical programming (MMP) formulation. The objectives functions of the proposed model include optimize the total fuel cost, the active power losses, and the system load ability. Since the control variables include discrete variables (var sources and transformer tap ratios), the ORPF is inherently a Mixed-integer nonlinear programming (MINLP) problem. The optimum tap settings of transformers are directly determined in terms of the admittance matrix of the network since the admittance matrix is constructed in the optimization framework as additional equality constraints. To show the effectiveness of proposed method the results are compared with single-objective, two-objectives and three-objectives. Finally, the method has been implemented in GAMS and solved using DICOPT solver to obtain optimal solutions. In this research, for selection of the best compromising answer among the Pareto optimal solutions has been designed a fuzzy decision-maker tool. The algorithm is tested on standard IEEE 14- bus test system. Simulation results show that the proposed algorithm is able to control reactive power flow effectively and optimize the selected objective functions.