SylabUZ
Nazwa przedmiotu | Operations Research 1 |
Kod przedmiotu | 11.1-WK-MATEP-OR1-S22 |
Wydział | Wydział Matematyki, Informatyki i Ekonometrii |
Kierunek | Mathematics |
Profil | ogólnoakademicki |
Rodzaj studiów | pierwszego stopnia z tyt. licencjata |
Semestr rozpoczęcia | semestr zimowy 2022/2023 |
Semestr | 6 |
Liczba punktów ECTS do zdobycia | 3 |
Typ przedmiotu | obieralny |
Język nauczania | angielski |
Sylabus opracował |
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Forma zajęć | Liczba godzin w semestrze (stacjonarne) | Liczba godzin w tygodniu (stacjonarne) | Liczba godzin w semestrze (niestacjonarne) | Liczba godzin w tygodniu (niestacjonarne) | Forma zaliczenia |
Wykład | 30 | 2 | - | - | Zaliczenie na ocenę |
Ćwiczenia | 30 | 2 | - | - | Zaliczenie na ocenę |
During the classes, students become familiar with the mathematical foundations of operations research, in particular the basics of linear programming and network issues. In addition, students learn basic problem-solving methods.
Linear Algebra 1 and 2, Calculus 1 and 2.
Lecture/classes
1. Model of the decision-making process. Operations research methods.
2. Linear programming models in operations research. The issue of production planning and the issue of diet.
3. Theoretical foundations of linear programming. Dualism in linear programming.
4. Methods for solving PL tasks - graphical method and simplex algorithm, dual simplex algorithm.
5. Transportation problem and transportation algorithm.
6. Discrete optimization and integer programming – sample models.
7. Methods for solving discrete optimization problems: Gomory cuts, branch and bound.
8. Network issues: shortest spanning tree, shortest paths, traveling salesman and methods of solving them.
Traditional lecture; auditorium exercises in which students solve tasks.
Opis efektu | Symbole efektów | Metody weryfikacji | Forma zajęć |
Exercises - written tests with point thresholds and tasks allowing to assess whether the student has achieved the learning outcomes.
Lecture - written test consisting of questions and tasks, verifying understanding of models and methods.
The final grade for the course takes into account the grade for exercises (50%) and the grade for lecture (50%). The condition for passing the course is a positive grade in the exercises and exam.
[1] D. Alevras and M.W. Padberg, Linear Optimization, Problems and Extensions, Springer-Verlag, Berlin 2001.
[2] W. J. Cook, W. H. Cunningham, W. R. Pulleyblank and A. Schrijver, Combinatorial Optimization, John Willey&Sons, New York 1998.
[3] J. Nocedal and S.J. Wright, Numerical Optimization, Second Edition, Springer, 2006.
[4] R.J. Vanderbei, Linear Programming, Foundations and Extensions, Kluwer, Boston 1997.
Zmodyfikowane przez dr Ewa Sylwestrzak-Maślanka (ostatnia modyfikacja: 21-02-2024 01:06)