Dr Nayat Horozoglu

This course is available on the BSc in Management, International Exchange (1 Term) and International Exchange (Full Year). This course is available as an outside option to students on other programmes where regulations permit and to General Course students.

This course has a limited number of places (it is capped). Places are allocated on a first come first served basis.

ST107 Quantitative Methods (Statistics) or equivalent. A core understanding of and practical experience with Microsoft Excel are required.

This course introduces students to concepts, techniques and applied aspects of the development and analysis of simulation models. Students will not only gain insights into simulation modelling, which is a widely used technique in real-life decision-making, but also develop valuable graduate skills such as communicating complex technical information to expert and non-expert audiences.

The course covers two main approaches for modelling problems bound by uncertainty (stochastic behaviour): Monte Carlo Simulations and Discrete Event Simulations. Topics of the course include structuring managerial problems for simulations, building and running simulation models using specialist software, and decision-making using simulation outputs.

Teaching hours in the WT will be commensurate with a usual half unit undergraduate course.

This course includes a reading week in Week 6 of the Winter Term, in line with Departmental policy.

In its Ethics Code, LSE upholds a commitment to intellectual freedom. This means we will protect the freedom of expression of our students and staff and the right to engage in healthy debate in the classroom.

One formative assessment will be set in WT.

The extensive lecture notes provided are the essential reading for this course. These lecture notes can be complemented with the following background readings:

• Banks, J., Carson, J., Nelson, B., & Nicol, D. (2013). Discrete-event system simulation. Harlow: Pearson Education.
• Rees, M. (2015). Business risk and simulation modelling in practice: Using Excel, VBA and @RISK. New York: John Wiley & Sons.
• Robinson, S. (2014). Simulation: The practice of model development and use (2nd ed.). London: Bloomsbury Publishing.
• Ross, S. M. (2012). Simulation (5th ed.). San Diego: Elsevier Science & Technology.

Project (100%) in the ST.