Course detail

Computing Methods in Logistics Optimization Problems

FSI-SOU-A Acad. year: 2025/2026 Summer semester

The course introduces the students to the algorithmic tools used for solving different types of optimization problems. The main content of the course lies in recognizing and using suitable methods for specific logistics problems.

Language of instruction

English

Number of ECTS credits

6

Entry knowledge

The presented topics require basic knowledge of concepts from optimization, statistics, and programming.

Rules for evaluation and completion of the course

Course-unit credit requirements: active participation in seminars, mastering the subject matter, and semester project acceptance.

Examination: Written exam focused on the successful implementation of the discussed methods accompanied by oral discussion of the results.

 

Attendance at seminars is required as well as active participation. Passive or missing students are required to work out additional assignments.

Aims

The emphasis is on the acquisition of application-oriented knowledge of logistics optimization methods, and on the use of computers and available software tools.

 

The student will acquire the ability to recognize a suitable optimization algorithm for a given logistics optimization problem. The student will be able to implement the said algorithm (alternatively, use an adequately chosen software tool) and perform a thorough analysis of the results.

The study programmes with the given course

Programme N-LAN-A: Logistics Analytics, Master's, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Syllabus

1. Introduction to optimization algorithms and 1D optimization


2. Descend direction methods, Grandient methods, Newton-type methods


3. Direct and stochastic optimization methods


4. Population-based methods for continuous problems


5. Penalty reformulations, Augmented Lagrangian


6. Interior point methods, barrier method, two-phase methods


7. Simplex method in matrix form, Integer and combinatorial optimization – Branch and Bound method, Gomory cuts


8. Local Search, Iterated Local Search, GRASP


9. Variable Neigborhood Search, Tabu Search, Simulated Annealing


10. Evolutionary Algorithms, Genetic Algorithms


11. Swarm Intelligence methods, Ant Colony Optimization


12. Multiobjective methods, NSGA-II, MOEA/D


13. Available software implementations, modular frameworks, automatic algorithm design (IRACE), modern approaches

Computer-assisted exercise

26 hours, compulsory

Syllabus

The exercise follows the topics discussed in the lecture. The main focus is on software implementation.