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Abstract

Optimization of production processes are required to provide companies with a competitive advantage. In the production industry this optimization is already under control and optimization of complex end-optimal models has been proposed. Using these model processes in agricultural production is inappropriate because they calculate exactly a process without the influence of random variables and factors. At the present time is usual to develop specific processes operating under ideal conditions. Their disadvantage is that real data from them vary considerably. Therefore, there is a need to develop simple methods and tools which can optimize these processes.

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