Stochastic Optimization and Risk Problems for Engineering Applications

In this paper a technique of handling Gaussian stochastic optimization problems is presented. These optimization problems are experienced in various industrial processes, where random disturbances take action directly on the constraints defining the operational process domain and on the parameters of the criterion function. The objective of our scientific work aims to find a solution to the stochastic problem reformulated as an equivalent deterministic optimization problem with totally admissible solutions. This problem is referred to as a risk problem, and it is handled using nonlinear mathematical programming approaches and represents the optimal decision for efficient exploitation of the industrial applications. A numerical Gaussian stochastic optimization case study for an optimal solar energy system exploitation is considered to validate the theoretical results.