Stochastic models of applied mathematics.

Course: Applied Mathematics

Structural unit: Faculty of Computer Science and Cybernetics

Title
Stochastic models of applied mathematics.
Code
ДВС.3.05
Module type
Вибіркова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2023/2024
Semester/trimester when the component is delivered
7 Semester
Number of ECTS credits allocated
4
Learning outcomes
PLO21.3. Understand the fundamental areas of mathematics and computer science, to the extent necessary for learning mathematical disciplines, applied disciplines and using their methods in a chosen profession. PLO22.3. Understand the main areas of mathematical logic, theory of algorithms and computational theory, programming theory, probability theory and mathematical statistics. PLO23.3. Be able to use professional knowledge, skills and abilities in the field of fundamental sections of mathematics and computer science for research of real processes of different nature. PLO24.3. Be able to independently analyze the relevant subject area, be able to develop mathematical and structural algorithmic models.
Form of study
Full-time form
Prerequisites and co-requisites
To successfully learn the discipline “ Stochastic models of applied mathematics. Queueing theory” the students should satisfy the following requirements. They know (a) fundamentals of mathematical methods for construction, verification and investigation of qualitative characteristics of deterministic and stochastic mathematical models; (b) classical methods of Calculus, Algebra and Probability Theory. They can (a) investigate qualitative characteristics of available mathematical models; (b) apply classical methods for solving applied problems in deterministic and stochastic models. They should be able to (a) apply classical methods of Calculus and Probability Theory; (b) seek information in open sources and properly analyze it.
Course content
Characteristics of queueing systems. Kendall's notation of queueing systems. Performance measures. Poisson flow. Characteristic property of exponential distribution. Birth-and-death processes. The simplest Markov models of queueing systems: M/M/1/∞, M/M/n/∞, and their stationary characteristics. Priority systems. System M/G/1. The virtual waiting time. The busy period. System G/G/1. Lindley's equation.
Recommended or required reading and other learning resources/tools
1. János Sztrik. Basic Queueing Theory. 2016. – 193 p. 2. Moshe Haviv. Queues - –A Course in Queueing Theory. Solution Manual 2015. – 89 p. 3. Cooper, R. Introduction to Queueing Theory, 3-rd Edition. CEE Press, Washington, 1990. – 361 p. 4. Ivo Adan and Jacques Resing. Queueing Theory. 2015. 180 pp. 5. http://web2.uwindsor.ca/math/hlynka/qonline.html. 2016. 6. Gross, D., Shortle, J., Thompson, J., and Harris, C. Fundamentals of queueing theory, 5th edition. John Wiley & Sons, New York, 2018. – 556 p. 7. O.K. Zakusylo. Lecture notes on queuing theory, 2023, 84 p.
Planned learning activities and teaching methods
Lectures, seminars, consultations, test works, independent work.
Assessment methods and criteria
Intermediate assesement: The maximal number of available points is 60. Test work no. 1: 30/18 points. Test work no. 2: 30/18 points. Final assessment (in the form of exam): The maximal number of available points is 40. The form of exam: writing. The types of assignments are 4 writing assignments (2 theoretical and 2 practical).
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Oleg K. Zakusylo
Operations Research
Faculty of Computer Science and Cybernetics

Departments

The following departments are involved in teaching the above discipline

Operations Research
Faculty of Computer Science and Cybernetics