DEVELOPMENT OF A GLOBAL METHOD OF SETTLEMENT PROBLEMS OF STOCHASTIC USING CHOPPED MIX NONLINEAR STOCHASTIC PROGRAM MODELS

This paper proposes a new approach to obtain a model of global optimization problem two-stage stochastic
program with nonlinearity present in the objective function and constraints. Variables in the first stage is worth the count, while the second phase variable mixture of chopped and continuous. The issue formulated by scenario-based representations. The basic idea to resolve the question of nonlinear mix chopped stochastic program is to transform the model into equivalent model in the form of deterministic nonlinear chopped-mix program. This is possible because the uncertainty is assumed to be spread discrete, can be modeled as a finite number of scenarios. However, the size of the equivalent model will grow rapidly as a consequence of a number of scenarios and the amount of time horizon. So that the number of scenarios may be limited (finite) techniques are required formation scenarios. The concept of a filtered probability space combined with data mining will be used for the formation scenarios. So as to obtain a method of settling the problem of large-scale nonlinear mix minced
program can be used convexity approach in order to obtain global optimal solution.