Course description
Programme
The School has a world-class Computational Optimization group. A graduate with an MSc in Operational Research with Computational Optimization would be attractive to companies who develop their own high performance optimization software and also to firms who are embedding optimization methods into their products. This MSc would also provide an ideal background for PhD studies in the area.
Learning Outcomes
Graduates will be able to identify appropriate Operational Research techniques to apply to practical problems, and select the mathematical techniques and software required to compute a solution.
Specifically, students will acquire the core skills of Java programming, the XPress linear optimization modelling language Mosel, the Simul8 simulation package as well as developing high-level applications of Excel.
Depending on your choice of optional courses, you will develop more specialist skills in computing, optimization and statistics, as well as an insight into a range of industrial and financial applications of Operational Research.
How You Will Be Taught
The MSc consists of traditional lecture-based courses and practical lab-based courses, assessed by exams, written reports and programming assignments. An industrial or academic project is written up as a dissertation.
Programme content and method of delivery
The study programme is divided into three parts. Part 1 covers the core skills of Operational Research: the majority of the core courses are in Semester 1 (S1) from September to December. Part 2 gives students the opportunity to tailor their degree to their own career objectives by selecting from a broad range of options: these are taught mainly in Semester 2 (S2) from January to March. Parts 1 and 2 are taken by both Diploma and MSc students. Part 3 is for the MSc only. It begins in June and comprises a three month project on which students base their dissertations. Projects are usually done in collaboration with outside organisations.
Parts 1 and 2 consist of taught courses. These are classified as core or optional. All core courses are compulsory and 100 hours of optional courses must be taken. Most courses are taught at a rate of 1 or 2 hours per week, but others are short intensive courses of two or three days duration. Several courses will include case studies.
There will be a Java course in Semesters 1 and 2, and Java and other computing skills will be used regularly throughout the programme. The MSc dissertations will normally involve some computational work. This can be modelling and solution of practical OR problems, statistical analysis as well as formal programming.
Core Courses
* Computing for Operational Research (10 hours S1 & 10 hours S2)
* Methodology, Modelling and Consulting Skills (10 hours S1 & 10 hours S2)
* Mathematical Programming (20 hours S1)
* Dynamic and Integer Programming and Games Theory (20 hours S1)
* Probability and Statistics (20 hours S1)
* Simulation (20 hours S1)
* Stochastic Modelling (20 hours S2)
Methodology, Modelling and Consulting Skills is taught using Xpress-MP
Xpress-MP Suite
Leading Optimization Software
Linear Programming
Mixed Integer Programming
Optional Courses
Students must choose optional courses with a total of 100 hours.
* Finance
o Credit Scoring and Data Mining (20 hours S2)
o Financial Mathematics and Investment (20 hours S1)
o Financial Model Building (10 hours S1)
o Management Accounting (10 hours S1)
* Risk
o Financial Risk Management (20 hours S2)
o Risk Analysis (10 hours S2)
* Industry
o Operational Research in the Airline Industry (10 hours S2)
o Operational Research in Telecommunications (10 hours S2)
* Energy
o Electrical Engineering Fundamentals of Renewable Energy (20 hours S1)
o Power Systems Engineering and Economics (40 hours S2)
o Operational Research in the Energy Industry (10 hours S2)
* Statistics
o Analysis of Lifetime Data (20 hours S2)
o Bayesian Analysis and Sample Size Estimation (10 hours S2)
o Statistical Modelling (10 hours S2)
o Time Series Analysis and Forecasting (10 hours S2)
* Optimization
o Continuous Global Optimization (10 hours S1)
o Combinatorial Optimization (10 hours S2)
o Large Scale Optimization (20 hours S2)
o Nonlinear Optimization (20 hours S1)
o Stochastic Optimization (10 hours S2)
* High Performance Computing (HPC)
o Practical Software Development (20 hours S1)
o Fundamental Concepts of High Performance Computing (20 hours S1)
o Applied Numerical Algorithms (20 hours S2)
o Exploiting the Computational Grid (20 hours S2)
A few restrictions on combinations of courses, together with any specific prerequisites, will be detailed in the programme documentation. A very small number of courses may not run in a particular year.
