MA331 Half Unit

Practical Optimisation Modelling

**This information is for the 2019/20 session.**

**Teacher responsible**

Dr Xue Lu

**Availability**

This course is available on the BSc in Business Mathematics and Statistics, BSc in Econometrics and Mathematical Economics, BSc in Economics, BSc in Management, BSc in Mathematics, Statistics, and Business and BSc in Statistics with Finance. This course is available as an outside option to students on other programmes where regulations permit and to General Course students.

**Pre-requisites**

Students must have a knowledge of Mathematics and Statistics to the level of MA107 (Quantitative Methods - Mathematics) and ST107 (Quantitative methods - Statistics), or ST102 (Elementary Statistical Theory). MA231 (formerly MG211) (Operational Research Methods) is not a prerequisite but is advisable to be taken previously or in conjunction with the course.

**Course content**

Mathematical Optimisation is one of the most powerful and widely-used quantitative techniques for making optimal decisions. The course has a pragmatic focus and aims at enabling students to model and solve real-life management problems. An overview is provided on fundamental technique, most importantly linear and integer programming, emphasising modelling and solution concepts and methods (e.g. feasibility, optimality, duality, multiple objectives, using binary variables for modelling, network models). The students will learn to formalise management problems using linear and integer programming models, to implement these models using specialised optimisation software, and to analyse and interpret the results, reflecting on the limitations of the models.

**Teaching**

15 hours of lectures and 13 hours and 30 minutes of classes in the MT. 1 hour and 30 minutes of classes in the LT.

8 hours of computer help sessions in the MT. 2 hours of computer help sessions in the LT. Computer help sessions are optional. Students on this course will have a reading week in Week 6, in line with departmental policy.

**Formative coursework**

Students will have the opportunity to submit a mock project for marking and feedback before the final assessed project is due. Students will also be given weekly homework exercises.

**Indicative reading**

H. Paul Williams: Model Building in Mathematical Programming, Fifth Edition, Wiley 2013.

F S Hillier and G J Lieberman: Introduction to Operations Research, McGraw-Hill

D Bertsimas and J N Tsitsiklis: Introduction to Linear Optimization, Athena Scientific.

A Ravindran, D T Philips, J J Solberg: Operations Research: Principles and Practice, Wiley.

**Assessment**

Project (100%) in the LT.

**
Key facts
**

Department: Mathematics

Total students 2018/19: Unavailable

Average class size 2018/19: Unavailable

Capped 2018/19: No

Value: Half Unit

**Personal development skills**

- Team working
- Problem solving
- Application of numeracy skills
- Specialist skills