Course detail
Stochastic Models in Logistics
FSI-SEP-AAcad. year: 2025/2026
The course provides an introduction to the theory of stochastic processes, covering key topics such as types and fundamental characteristics of stochastic processes, time series decomposition, Markov chains, Poisson processes, and queueing theory. Students will gain practical skills in application of this methods in describing and predicting stochastic processes using appropriate software tools.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Entry knowledge
Rudiments of probability theory and mathematical statistics, linear regression models.
Rules for evaluation and completion of the course
Course-unit credit requirements: active participation in seminars, demonstration of basic skills in practical data analysis on PC in a project, and succesfull solution of possible written tests.
Examination: oral exam, questions are selected from a list of 3 set areas (30+30+40 points). At least a basic knowledge of the terms and their properties is required in each of the areas. Evaluation by points: excellent (90 - 100 points), very good (80 - 89 points), good (70 - 79 points), satisfactory (60 - 69 points), sufficient (50 - 59 points), failed (0 - 49 points).
Attendance at seminars is compulsory whereas the teacher decides on the compensation for absences.
Aims
The course objective is to make students familiar with the principles of the theory of stochastic processes and models used for their analysis. At seminars, students apply theoretical procedures on simulated or real data using suitable software. The semester is concluded with a project of analysis and prediction of a real stochastic process.
The course provides students with basic knowledge of modeling stochastic processes (time series decomposition, Markov chains, Poisson processes, Queueing theory) and ways to estimate their assorted characteristics in order to describe the mechanism of the process behavior on the basis of its observations. Students learn basic methods used for real data evaluation which might be encountered in logistics.
Study aids
Prerequisites and corequisites
Basic literature
Grimmett, G., Stirzaker, D.: Probability and random processes. Oxford; New York: Oxford University Press. 2001. (EN)
Tijms, H.C. A First Course in Stochastic Models, John Wiley & Sons, 2003. 478 p. ISBN:9780471498803 (EN)
Shortle, J.F., Thompson, J.M., Gross, D., Harris, C.M. Fundamentals of Queueing Theory, 5th ed. John Wiley & Sons, 2018. 576 p. ISBN: 978-1-118-94352-6 (EN)
Shumway, R., Stoffer, D. Time Series Analysis and Its Applications With R Examples. Springer, 2017. 978-3-319-52452-8. (EN)
Recommended reading
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
- Stochastic process: types, fundamental properties, stationarity.
- Decomposition model and estimation of individual components (smoothing, polynomial regression).
- Trend estimation with seasonality. Randomness tests.
- Autocorrelation function, partial autocorrelation function, and cross-correlation.
- Markov chains I.
- Markov chains II.
- Random walk, generating functions.
- Continuous-time Markov processes.
- Poisson processes.
- Birth-and-death processes.
- Queueing systems.
Computer-assisted exercise
Teacher / Lecturer
Syllabus
- Input, storage, and visualization of data, simulation of stochastic processes, queueing systems especially.
- Decomposition model and estimation of individual components (smoothing, polynomial regression, Box-Cox transformation).
- Trend estimation with seasonality. Randomness tests.
- Autocorrelation function, partial autocorrelation function, and cross-correlation.
- Markov chains I.
- Markov chains II.
- Random walk, generating functions.
- Continuous-time Markov processes.
- Poisson processes.
- Birth-and-death processes.
- Queueing systems
- Tutorials on student projects