Stochastic Modeling

COURSE OBJECTIVE
Within this course you will get acquainted with stochastic processes and models for waiting lines (queueing models). The learning objectives are:

• To know the assumptions and formulations of some fundamental stochastic processes and queueing models.
• To be able to analyze the fundamental models mentioned above and apply similar analysis techniques to related models.
• To formulate a model based on a practical situation and recognize which model is applicable.
• To be able interpret the final result of stochastic models and understand the practical implications (like economies of scale, impact of variability and critical load).

COURSE CONTENT
Stochastic processes and queueing models are often applied to model practical situations where uncertainty is involved. This course mainly focuses on Markov chains and queueing models. A key element is the theoretical development of such models with the emphasis on modeling and its analysis. In addition, the models are motivated by applications. More specifically, the fundamental stochastic processes and queueing models that we study are: Markov chains in discrete and continuous time, the Poisson process, the M/M/1 queue, the Erlang delay and loss model, birth-death processes, the M/G/1 queue and the waiting-time paradox.

FORM OF TUITION
Lectures and tutorials.

TYPE OF ASSESSMENT
Two mid-term exams and a hand-in assignment in period 1 (presented in the 4th week that should be turned in 2 weeks later). The resit involves all material.

RECOMMENDED BACKGROUND KNOWLEDGE
Probability theory, Calculus 1 & 2, Linear Algebra