Dr Alicia Mejia-Salazar

This course is available on the BSc in Business Mathematics and Statistics, BSc in Management and BSc in Statistics with Finance. This course is available as an outside option to students on other programmes where regulations permit and to General Course students.

Elementary statistical concepts and experience of standard computer software is assumed.

The main characteristic of this course is that it is a hands-on course and of an extremely practical nature. Research shows that 90% of the largest organisations both in Europe and the USA use the techniques taught here to monitor their operations and especially in risk management. The aim of the course is to introduce students to the concepts, techniques and applied aspects of the development and analysis of simulation models. The course will cover two main approaches for modelling problems bound by uncertainty (stochastic behaviour): Monte-Carlo Simulation (static problems) and Discrete Event Simulation (dynamic problems). Topics covered will include: types of uncertainty; types of simulation modelling; sampling methods; the simulation process; structuring problems for simulation; running simulation models; analysing simulation outputs; risk analysis using simulated models; testing and validating simulation models; applications of simulation. Excel modelling is an integral part of Monte Carlo simulation and at the end of the course students will have a sound foundation on how to set up different Excel models. Additional tutorial examples will be provided both throughout the course, and posted on Moodle to help develop this very important skill.

10 hours of lectures and 10 hours of classes in the LT.

An Excel help class may be held during reading week in Week 6.

Extended office hours to students who need it.

Three individual or small-group assignments will be required during the course.

JR Evans & DL Olson (2002) Introduction to Simulation and Risk Analysis. Prentice-Hall: Upper Saddle River, NJ; AM Law (2006) Simulation Modelling and Analysis. McGraw-Hill: Boston, 4th ed.; M Pidd (2004) Computer Simulation in Management Science. Wiley: Chichester, 5th ed.; S Robinson (2004) Simulation - The Practice of Model Development and Use, Wiley: Chichester; D Vose (2008) Risk Analysis - A Quantitative Guide, Wiley: Chichester, 3rd ed.

Project (100%) in the ST.

An individual management report (maximum 15 pages – excluding appendix) describing the modelling and results from a simulation study of a realistic decision problem. The problem will be defined by week 5 or 6 of the LT, the project should be completed by the beginning of the ST.

To view the course guide video, please click the following link: http://richmedia.lse.ac.uk/management/20160606_UGcourse_MG308.mp4