Provides a solid background to the mathematical and computing theory that underpins the OR techniques introduced in other modules, and in particular, Applied Optimisation.
An introduction to a range of standard operational research techniques, including scheduling, critical path analysis (CPA), linear programming and dynamic programming, neural netowrks and support vector machines. “Soft OR” and problem structuring methods (PSMs) will also be considered.
Covers the use of mathematical probability to inform problem-solving, including its use in queuing theory as a precursor to study of event simulation.
A mathematical approach to investigating the solvability of a problem, including working with and developing algorithms.
Introduces a range of techniques for informing decision making strategies, including those which must incorporate multiple decision criteria.
Data obtained from observations collected sequentially over time are extremely common. The purpose of time series analysis is to understand or model the stochastic mechanism that gives rise to an observed series and to predict or forecast the future values of a series based on the history of that series, and, possibly, other related series or factors.
The quantity of data available to analysts is growing at an ever-increasing rate. This data has become a vital tool for decision-making in a competitive world. However, the size, which makes the data so valuable, also makes it difficult to analyse using traditional statistical methods. This module introduces the student to a variety of methodologies now employed to explore, analyse, categorise and visualise data from large data sets.
You can find more information about this course in the programme specification. Module and programme information is indicative and may be subject to change.