Mathematical Methods in Logistics (FSI-SMA-A)

Academic year 2026/2027
Supervisor: doc. Mgr. Petr Vašík, Ph.D.  
Supervising institute: ÚM all courses guaranted by this institute
Teaching language: English
Course type: departmental course
Aims of the course unit:
 
Learning outcomes and competences:
 
Prerequisites:
 
Course contents:
 
Teaching methods and criteria:
 
Assesment methods and criteria linked to learning outcomes:

Credit will be awarded for the completion of a semester project. This will involve the independent development of a genetic algorithm for solving a combinatorial optimization problem in logistics. The exam will take the form of a project defense, which will be assigned no later than the 10th week of the semester.
Controlled participation in lessons:
 
Type of course unit:
    Lecture  13 × 2 hrs. optionally                  
    Exercise  13 × 1 hrs. compulsory                  
Course curriculum:
    Lecture

Week 1-3: Introduction to convex optimisation, convex functions, convex sets
Week 4-5: Quadratic programming


Week 6- 9: Evolutionary algorithms with an emphasis on genetic algorithms


Week 10-13: Implementation techniques and algorithm design for solving the so-called Green TSP problem
    Exercise

In the first exercise we recall elementary notions from analytical geometry and numerical methods. Tutorial examples will be calculated. Further exercises will topically follow the lectures from the previous week.
Literature - fundamental:
1. W. Forst and D. Hoffmann,  Optimization―Theory and Practice, Springer Undergraduate Texts in Mathematics and Technology, 2010th Edition, [2010].
2. M. Athans and P. L. Falb, Optimal control: an introduction to the theory and its applications. Mineola: Dover Publications, [2007]. 
3. M. H. Veatch, Linear and Convex Optimization: A Mathematical Approach, Wiley, [2021].
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
N-LAN-A full-time study --- no specialisation -- Cr,Ex 5 Compulsory 2 1 S
C-AKR-P full-time study CLS -- Cr,Ex 5 Elective 1 1 S