1. Issues of optimal process control. Optimization of static and dynamic systems. Introduction to optimal control theory, static and dynamic optimization, one-dimensional and multidimensional problems, mathematical apparatus and methods of solution.
2. Analytical methods of static one-dimensional optimization, derivation of necessary and sufficient conditions for optimal search, approaches and methods of solution. Practical graphical representation of methods in Excel.
3. Numerical methods of static one-dimensional optimization, their importance and individual approaches. Practical use of differential, direct, interpolation, comparative, adaptive methods.
4. Analytical methods of multidimensional static optimization without limitations and limitations.
5. The smallest square method uses it to approximate functions. Using neural networks to approximate functions. Compare both approaches.
6. Numerical methods of multidimensional static optimization, deterministic and stochastic. Using artificial intelligence in solving multidimensional static optimization tasks. Software implementation of these methods with simulations.
7. Principles and methods of extreme regulation and examples of their practical use in metallurgy and related fields. Software implementation of the extreme controller.
8. Linear programming, basic concepts, graphical interpretations and solutions, modeling and application at hierarchically superior management levels in the metallurgical industry.
9. Linear programming - solution of production programming tasks, nutritional problem, distribution problem and optimization of cutting plans.
10. Practical use of solver in Excel for solving linear and nonlinear problems of multidimensional static optimization.
11. Methods of optimization in solving practical problems in logistics.
12. Dynamic optimization, basic concepts, types of purposeful functions, task definition, methods and applications for optimal control of larger energy aggregates and metallurgical units and optimal setting of control circuits.
13. Use of genetic algorithms and solver in Excel for optimizing the acquisition of mathematical descriptions of dynamic systems from experimentally acquired processes.
2. Analytical methods of static one-dimensional optimization, derivation of necessary and sufficient conditions for optimal search, approaches and methods of solution. Practical graphical representation of methods in Excel.
3. Numerical methods of static one-dimensional optimization, their importance and individual approaches. Practical use of differential, direct, interpolation, comparative, adaptive methods.
4. Analytical methods of multidimensional static optimization without limitations and limitations.
5. The smallest square method uses it to approximate functions. Using neural networks to approximate functions. Compare both approaches.
6. Numerical methods of multidimensional static optimization, deterministic and stochastic. Using artificial intelligence in solving multidimensional static optimization tasks. Software implementation of these methods with simulations.
7. Principles and methods of extreme regulation and examples of their practical use in metallurgy and related fields. Software implementation of the extreme controller.
8. Linear programming, basic concepts, graphical interpretations and solutions, modeling and application at hierarchically superior management levels in the metallurgical industry.
9. Linear programming - solution of production programming tasks, nutritional problem, distribution problem and optimization of cutting plans.
10. Practical use of solver in Excel for solving linear and nonlinear problems of multidimensional static optimization.
11. Methods of optimization in solving practical problems in logistics.
12. Dynamic optimization, basic concepts, types of purposeful functions, task definition, methods and applications for optimal control of larger energy aggregates and metallurgical units and optimal setting of control circuits.
13. Use of genetic algorithms and solver in Excel for optimizing the acquisition of mathematical descriptions of dynamic systems from experimentally acquired processes.