342-0963/02 – Stochastic Computational Methods (SVM)
| Gurantor department | Institute of Transport | Credits | 10 |
| Subject guarantor | doc. Ing. Michal Dorda, Ph.D. | Subject version guarantor | doc. Ing. Michal Dorda, Ph.D. |
| Study level | postgraduate | Requirement | Choice-compulsory type B |
| Year | | Semester | winter + summer |
| | Study language | English |
| Year of introduction | 2019/2020 | Year of cancellation | |
| Intended for the faculties | FS | Intended for study types | Doctoral |
Subject aims expressed by acquired skills and competences
The student is able to characterize the individual types of queueing systems and can define what input data he / she needs to obtain for modeling the solved queueing system. These data can be processed by appropriate statistical methods. He has an overview of the mathematical models of queueing systems and can use these models to solve practical problems. It controls the modeling methods of queueing systems and is able to create mathematical models of queueing systems. Can use colorful Petri nets for modeling and simulation of queueing systems.
Teaching methods
Lectures
Individual consultations
Project work
Summary
The course is devoted to methods of modeling and simulation of queueing systems. The student is acquainted with the methods used for modeling of mass control systems in time (so called transition analysis) and in steady state. Within the subject are discussed the models of individual mass control systems differing in assumptions, from the elementary Markov models to models requiring a more demanding mathematical apparatus. For simulation of mass control systems, the Petri color network is used.
Compulsory literature:
Recommended literature:
Additional study materials
Way of continuous check of knowledge in the course of semester
Oral examination.
E-learning
Other requirements
Solution and defense of the project on the given topic.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1) Selected pieces of knowledge of probability theory - generating functions, random variables used in the queueing theory, convolution.
2) Baysian statistical principles.
3) Stochastic programming.
4) Theory of random processes with continuous and discrete time.
5) Advanced knowledge of the queueing theory - methods of input flow modeling, methods of modeling of the operating time, methods of calculation of performance measures.
6) Markov queueing systems and methods of their modeling in time (transition analysis).
7) Markov queueing systems and their modeling methods in steady state.
8) Modeling of queueing systems with Erlang input flow and / or Erlang service time.
9) Modeling of M/D/1, M/G/1 and G/M/1 queueing systems.
10) Multi-operator systems with service lines that do not work continuously (due to malfunctions, maintenance, etc.).
11) Queueing networks and their modeling.
12) Possibilities of computer modeling of queueing systems (Witness, colored Petri nets).
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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