Dr Xue Lu

This course is compulsory on the MSc in Management Science (Decision Sciences) and MSc in Operations Research & Analytics. This course is available on the Global MSc in Management, Global MSc in Management (CEMS MIM), Global MSc in Management (MBA Exchange) and MSc in Data Science. This course is available with permission as an outside option to students on other programmes where regulations permit.

Students must know basics of linear algebra (matrix multiplication, geometric interpretation of vectors) and probability theory (expected value, conditional probability, independence of random events).  

Students taking the course as an outside option are also expected to have a basic knowledge of linear programming. For students in the MSc in Operations Research & Analytics this will be covered in MA423 Fundamentals of Operations Research.

The course will be in 2 parts, covering the two most prominent tools in operational research: simulation, the playing-out of real-life scenarios in a (computer-based) modelling environment, and mathematical optimisation, the application of sophisticated mathematical methods to make optimal decisions.

Simulation (8 lecture hours): This part develops simulation modelling skills, understanding of the theoretical basis which underpins the simulation methodology, and an appreciation of practical issues in managing a simulation modelling project. Topics include Monte Carlo simulation, Markov processes, discrete event simulation, and variance reduction. The course will teach students how to use a simulation modelling software package.

Optimisation (12 lecture hours): This part enables students to model and solve real-life management problems as Mathematical Optimisation problems. In providing an overview of the most relevant techniques of the field, it teaches a range of approaches to building Mathematical Optimisation models and shows how to solve them and analyse their solutions. Content includes: The modelling life-cycle and modelling environments; formulation of management problems using linear and network models; solution of such problems with a special-purpose programming language; interpretation of the solutions; limitations of such nodels; and formulation and solution of nonlinear models including some or all of binary, integer, convex and stochastic programming models.

20 hours of lectures, 13 hours and 30 minutes of seminars and 18 hours of computer workshops in the MT.

Computer workshops are not mandatory. They are help sessions, where an instructor is available to students in the computer cluster while they work on their assignment. 

Students will be expected to produce 2 projects in the MT.

Feedback will be provided on the weekly homework.  Additional feedback will be provided on a one-on-one basis to students attending the optional computer help sessions.

Full lecture notes will be provided to students for both parts.

Recommended readings:

Simulation

• A M Law & W D Kelton, Simulation Modelling and Analysis, McGraw Hill (3rd ed., 2000);
• M Pidd, Computer Simulation in Management Science, Wiley (5th ed., 2006);
• S Ross, Simulation, Academic Press (5th ed., 2012).

Optimisation

• W L Winston, Operations Research: Applications and Algorithms, Brooks/Cole (4th ed., 1998);
• D Bertsimas and J N Tsitsiklis: Introduction to Linear Optimization, Athena Scientific (3rd ed., 1997).

Project (50%) and project (50%) in the LT.

The two pieces of assessed work in the LT are one on Simulation (50%) and one on Mathematical Optimisation (50%). The deliverable for each project is a report of at most 12 pages (not including technical appendices), along with a soft copy of any computer code and solver output.