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
Supervisor
Department
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.