Course Information Undergraduate prospectus

Operational Research for Industry

Course summary

Course code: STAT1026
Level: 5
Credits: 30
School: Architecture, Computing and Hums
Department: Mathematical Sciences
Course Coordinator(s): Timothy Reis


Pre and co requisites

Advanced Calculus and Mathematical Models.


As the structure and interdependency of companies becomes more and more complex, management requires the expertise of staff trained in Operational Research (OR) to assist in both long and short term planning and logistics. The aim of the course is to introduce students to standard models of Operational Research and give them knowledge of both analytical and software-based OR techniques. Students will be encouraged to think independently, analytically and creatively, and engage imaginatively with new areas of investigation.

Learning outcomes

On successful completion of this course a student will be able to:

1. Demonstrate an understanding of the theory behind the standard OR formulae and algorithms presented.
2. Formulate and solve elementary problems requiring the use of these formulae and algorithms, both analytically and using appropriate software.
3. Formulate and solve linear programmes graphically, by the simplex method and a suitable computer package.
4. Interpret the output from the computer package in terms of the original problem (including sensitivity results).
5. Obtain specific non-uniform random numbers from uniform ones, and to use them in elementary simulation problems.
6. Use software tools to handle various simulation models.
7. Develop and validate mathematical models of real-life situations.
8. Present mathematical conclusions to a lay audience through presentations and reports.
9. Evaluate your readiness for employment in the area of your choice and formulate an action plan to acquire the necessary skills.
10. Prepare an application for a job relevant to your degree.

Indicative content

Simulation: application to simple problems of OR, business and physical systems (e.g. queueing, inventory control). Introduction to software capable of performing simulation (SIMUL8 and Microsoft Excel).
Linear Programming: graphical, simplex and computer solutions (using Microsoft Excel), sensitivity analysis, the transportation problem.
Inventory control: models with deterministic and probabilistic demand.
Queueing theory: models with Poisson arrival, steady-state analysis, cost analysis.
Modelling Week - The modelling process; tools for constructing and analysing models; validation of models; checking if the answers are plausible.
Employability: preparing for the workplace, researching opportunities, making an application, planning for a career.

Teaching and learning activity

Concepts and algorithms introduced in lectures - 50 % (all learning outcomes);
Concepts and algorithms practised in tutorials - 20% (all learning outcomes);
Independent work using software tools - 20% (learning outcomes 2, 3, 4, 5, 6);
Groupwork - Modelling Week (intensive learning outcomes 7 and 8);
Researching and assembling portfolio - 10% (learning outcomes 9 and 10).


Summative assessment:

Coursework 1 - 10%
A coursework to assess learning outcomes 1, 2, 4-8.

Coursework 2 - 10%
A coursework to assess learning outcomes 1-4, 7, 8.

Group Coursework - 20%
Group presentation and group report presenting solution of Modelling Week problem to a lay audience. LO - 7,8.

PDP Portfolio - 10%
A Portfolio showing student's research into career opportunities, mock application, self-evaluation and self-development plans. LO - 9,10.

Exam - 50%
Final examination assessing learning outcomes 1-6.

Formative assessment: Weekly tutorial exercises.