Models and Methods of Operations Research

Teaching results

Course objective: Scientific advances in economics and operational research make it possible to model dependencies and conditions more accurately, to quantify the stochasticity of input data encountered by the solver, to implement the time aspect, and to calculate the optimal solution to complex decision problems. Modern models and methods of operational research emphasize the stochastic aspect of input data and the dynamic aspect of solved economic processes. The application dimension of operational research models is currently focused on the environmental area, the area of distribution, energy efficiency and the like. The aim of the course is to more accurately model the dependencies and conditions, quantify the uncertainty encountered by the decision maker, implement the time aspect and calculate the optimal solution to complex decision problems.
Knowledge and understanding.
After studying this course, students should:
a) understand the principles and principles of creating complex operational research models,
b) know the methodologies and relevant tools of problem analysis.
Skills, characteristics and attributes.
After studying this course, students should be able to:
a) compile appropriate mathematical programming models emphasizing data uncertainty and the dynamic aspect of decision-making tasks;
b) apply different methods using existing methodologies and approaches to the analysis of the systems under investigation;
c) be able to use practical tools to create appropriate mathematical programming models;
d) make efficient use of tools to solve corresponding mathematical programming, stochastic programming, fuzzy mathematical programming and dynamized mathematical programming problems.
Upon successful completion of the course, students will acquire the following competencies:
- practical skills and competencies with the application of methods and algorithms in mathematical modeling of complex economic processes and subsequent problem solving.