Course detail

Computing Methods in Logistics Optimization Problems

FSI-SOU-AAcad. year: 2025/2026

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

Mode of study

Not applicable.

Guarantor

doc. Ing. Jakub Kůdela, Ph.D.

Department

Institute of Automation and Computer Science (ÚAI)

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.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Martí, R. Pardalos, P.M., Resende, M.G.C.: Handbook of Heuristics. Springer Cham, 2018. (EN)
Kochenderfer, M.J., Wheeler, T.A.: Algorithms for Optimization. MIT Press, 2019. (EN)
Martins, J.R.R.A., Ning A.: Engineering Design Optimization. Cambridge University Press, 2021. (EN)
Rardin, R. L.: Optimization in Operations Research. Pearson, 2015. (EN)
Williams, H.P.: Model Building in Mathematical Programming. J. Wiley and Sons, 2012. (EN)

Recommended reading

Langevin, A., Riopel, D. Logistics Systems: Design and Optimization. Springer, 2005. (EN)

Classification of course in study plans

  • Programme N-LAN-A Master's 1 year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Jakub Kůdela, Ph.D.

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 hod., compulsory

Teacher / Lecturer

doc. Ing. Jakub Kůdela, Ph.D.

Syllabus

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