Introductory description

This is the core module taught in term 1. The main objective of the module is to provide sound theoretical backgrounds for the further use in elective modules in terms 2 and 3. To encourage active learning the module includes a variety of real life applications of optimisation models in analytics and data analysis. It also includes an assignment that involves individually generated tasks to be solved by using nowadays computational technologies.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Optimisation modelling: mathematical programming, including extensive studies of linear programming and dynamic programming; integer programming, introduction to non-linear optimisation, introduction to algorithms and heuristics. The techniques mentioned are illustrated on a range of real life applications in business analytics and data science. IT tools (Excel, R or Python libraries, etc.) are used to demonstrate the usage of the theory in practice.

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate knowledge of optimisation techniques and effective problem solving and decision making skills.
• Develop an adequate methodology and skills to extract, formalise and solve a structured model for analysis of messy real life decision support problems

Indicative reading list

C. Albright and W. Winston, Management Science Modeling, Thomson/South-Western, 2011
Bradford Tuckfield , Dive Into Algorithms: A Pythonic Adventure for the Intrepid Beginner, 2021

P.Zörnig, Nonlinear Programming: An Introduction, Berlin, Boston: De Gruyter, 2014. https://0-doi-org.pugwash.lib.warwick.ac.uk/10.1515/9783110315288

J. VanderPlas, Python Data Science Handbook: Tools and Techniques for Developers: Essential Tools for Working with Data, O’Reily, 2016.

W.L.Winston (any addition) Operations Research. Applications and algorithms, International Thompson Publishing.

Research element

Elements of research methodology typical for quantitative analysis (mathematical, analytical, computational approaches) are represented in examples discussed in the class

Interdisciplinary

The module includes a use of computer science and algorithms as a part of teaching material; that illustrate an interdisciplinary nature of approaches taught.

Subject specific skills

Formulate a model; Select the most efficient method to tackle the problem; Use appropriate software to solve the model; Report on the findings using a range of media which are widely used in business; Reflect on the validity of the model and the modelling process accepted.

Identify problem structures, to suggest solution approaches and to identify potential quality issues of the solution

Transferable skills

Numeracy skills
Written communication