4EK632 Optimization Modelling
Aims of the course:
The aim of the course is to acquaint students with optimization methods and models and other techniques for decision-making in the economic environment.
Upon successful completion of this course, students will be able to apply standard optimization models for solving real-life decision-making problems. They will also be familiar with selected software packages for mathematical modeling and optimization.
- Traditional application areas of optimization models. Classification of optimization techniques.
- Introduction to linear programming (LP). Objectives, constraints, decision variables, and solutions.
- Formulation of typical LP problems. Manufacturing, transportation, and nutrition problems.
- Solving LP problems and interpreting the results. Graphical solution and general algorithms. Sensitivity analysis.
- Software support for LP problems.
- Mixed-integer linear programming problems (MILP). Assignment, traveling salesman, set-covering, and crew-scheduling problems.
- MILP and computational limits. Heuristics for selected problems, meta-heuristics.
- Optimization problems on graphs. Optimal paths and flows, LP formulation, and its comparison against custom-tailored algorithms.
- Project management and optimization, CPM, and PERT methods.
- Deterministic and stochastic inventory models. Optimizing order times and quantities.
- Queueing models. Applications of queuing models in computer science, manufacturing, and services. Analytic and simulation approach.
- Multiple criteria decision-making. Multiple-criteria evaluation and design problems, solution principles.