Computing Methods in Logistics Optimization Problems (FSI-SOU-A)

Academic year 2025/2026
Supervisor: doc. Ing. Jakub Kůdela, Ph.D.  
Supervising institute: ÚAI all courses guaranted by this institute
Teaching language: English
Aims of the course unit:

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.
Learning outcomes and competences:
 
Prerequisites:
 
Course contents:

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.
Teaching methods and criteria:
 
Assesment methods and criteria linked to learning outcomes:

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.
Controlled participation in lessons:
 
Type of course unit:
    Lecture  13 × 2 hrs. optionally                  
    Computer-assisted exercise  13 × 2 hrs. compulsory                  
Course curriculum:
    Lecture

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

The exercise follows the topics discussed in the lecture. The main focus is on software implementation.
Literature - fundamental:
1.

Williams, H.P.: Model Building in Mathematical Programming. J. Wiley and Sons, 2012.
2.

Rardin, R. L.: Optimization in Operations Research. Pearson, 2015.
3.

Kochenderfer, M.J., Wheeler, T.A.: Algorithms for Optimization. MIT Press, 2019.
4.

Martins, J.R.R.A., Ning A.: Engineering Design Optimization. Cambridge University Press, 2021.
5. Martí, R. Pardalos, P.M., Resende, M.G.C.: Handbook of Heuristics. Springer Cham, 2018.
Literature - recommended:
1.

Langevin, A., Riopel, D. Logistics Systems: Design and Optimization. Springer, 2005.
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 6 Compulsory 2 1 S