The Model of Stochastic Optimization of Production under Uncertainty and Risk

Abstract

The article describes possibilities of application of models of stochastic optimization of output. The features of the application of various problems of stochastic optimization. The expediency of application of methods of network planning method, PERT, and in the process create the network model of production. It is shown that in conditions of uncertainty and risk when it is difficult to accurately determine the values of the parameters of the problem the use of stochastic models in the planning process of production allows to achieve higher profitability. The authors propose a stochastic model of optimal output and practical recommendations for its application in the activities of industrial enterprises is concluded that to improve the efficiency of planning processes of production it is advantageous to combine the proposed stochastic optimization model with the method of analysis and evaluation of programs.

Models of optimization of combination of production of agrarian products and harvesting of wild food resources

“…where t is parameter. Model (7), (8), (11) -(13), (14), (15) involves the use of one parameter. At the same time, it is possible to apply deterministic multi-parameter linear programming problems to describe real situations.…”

“…Obviously, to build models to optimize the combination of agricultural production and harvesting of wild plants, it is necessary to use mathematical programming methods in conditions of uncertainty. Here are some examples of modeling of receiving products with random and interval parameters [15][16][17][18][19]. In work [15], it was proposed to use stochastic methods in network planning of industrial production.…”

“…Here are some examples of modeling of receiving products with random and interval parameters [15][16][17][18][19]. In work [15], it was proposed to use stochastic methods in network planning of industrial production. The author of the article [16] pays great attention to multistage stochastic programming.…”