URL study guide
https://tue.osiris-student.nl/onderwijscatalogus/extern/cursus?cursuscode=2MBS70&collegejaar=2026&taal=enDescription
Queueing phenomena are encountered in many real-life situations. Prominent examples are service counters, elevators and traffic networks, but queueing effects also arise in supply chains, production systems, and communication networks. In this course, you'll learn basic mathematical models for analyzing congestion effects in terms of queue lengths and waiting times. You'll also develop insight in applications of such approaches for improving the design and performance of service operations.Queueing models use basic probability concepts and stochastic processes (Poisson processes, Markov chains, birth-death processes, random walks) to describe and analyze congestion effects in terms of queue lengths and waiting times. In this course, we introduce the most common models in queueing theory, such as M/M/1, M/M/c, M/G/1 and G/M/1, discuss their fundamental properties, and explain how these models arise in various scenarios of interest. The focus is on mathematical techniques for deriving the stationary queue length distribution and waiting-time distribution, and calculating several specific performance measures. We also discuss how these methods and results can be applied for improving the efficiency or evaluating the performance of real-life service facilities.
Objectives
Learning objectives:- Obtaining knowledge of the most common models in queueing theory
- Developing insight in several fundamental properties of queueing systems and the impact of various stochastic characteristics
- Gaining familiarity with basic techniques for analyzing queueing models (using probability generating functions and Laplace-Stieltjes transforms)
- Acquiring a sense how methods and results from queueing theory can be applied for improving the efficiency or evaluating the performance of real-life service facilities in society, industry and technology