Discrete Decision Making

Entry requirements: Basic knowledge of probability theory, statistics and programming.

Credits: 5

Semester: 3

Course: Elective

Language of the course: English

Lecturer

Sergey Ivanov

Objectives

Students will learn: Understand principles of modeling and simulation; Simulate examples with random input; Build Discrete-Events models; Use statistical models for generation of input data;Build and Run models of queueing, inventory and supply-chain systems;Analyze simulation;Estimate model performance.

Contents

The main objective of the course is to provide a basic treatment of the important aspects of discrete (mainly discrete-event) simulation, with particular emphasis on examples to illustrate simulation principles and applications in manufacturing, services, and computing. The course includes general principles of simulation, popular statistical models, random input modeling, and analysis of simulation data. The course is independent of any particular simulation language, while most of the examples are shown using the Python language. Having completed the course, you'll be able to choose the right tools for simulation, understand the advantages and disadvantages of a particular model, make a performance assessment, and know how to carry out a simulation project.

Format

Laboratory works.

Assessment

Attendance is mandatory. Students cannot miss more than one class.

Grading: 40% individual assignments; 60% final exam.