Operational Research Methods

This information is for the 2020/21 session.

Teacher responsible

Dr Giacomo Zambelli

Dr Matoula Kotsialou


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


Mathematics, Statistics and Probability Theory to the level of the courses MA107 Quantitative Methods (Mathematics) and ST107 Quantitative Methods (Statistics) is required. In particular, students should have covered elementary distribution theory and the Poisson Process, and have an elementary knowledge of linear algebra. Students must be prepared to use computer packages when required. 

Course content

An introduction to all the main theoretical techniques of Operational Research.

Linear optimisation: from the most basic introduction to sufficient conditions for optimality; duality; sensitivity of the solution; discovery of the solution to small problems by graphical methods, and proof of optimality by testing the sufficient conditions. The transportation problem. Modelling real world problems using linear optimisation.

Various other operational research techniques including: Shortest Paths, Critical Path Analysis, Markov Chains, Stable Matchings, Queueing Theory, Simulation, Inventory Management, Dynamic Programming, Decision Theory, Game Theory.

The course includes an assessed software component. The software used will be "Microsoft Excel" and the add-on packages "LP solve" to solve linear optimisation problems and "@ risk" to perform Monte Carlo simulation.

Full lecture notes are provided.


This course is delivered through a combination of classes and lectures totalling a minimum of 60 hours across Michaelmas Term and Lent Term. This year, some or all of this teaching will be delivered through a combination of virtual classes and lectures delivered as online videos.

Formative coursework

Students will be expected to produce 10 problem sets in the MT and 1 project and 5 problem sets in the LT.

The formative coursework comprises weekly problem sets. A mock project will be given, similar in format to the summative project, to be carried out by the same groups that will work on the final project. This is meant as a trial run of the group project, with a similar level of work but with no summative mark.

Indicative reading

Comprehensive lecture notes will be provided. The course content largely follows the following textbook:

  • F S Hillier, G J Liebermann, Introduction to Operations Research, McGraw-Hill Series in Industrial Engineering and Management Science. Any edition from 7th onward.

Further reading includes:

  • W L Winston, Operations Research, Duxbury Press (2004).
  • W L Winston, S C Albright: Practical Management Science, Cengage Learning. 4th edition or later.
  • H P Williams, Model Building in Mathematical Programming, Wiley (2013).


Exam (80%, duration: 2 hours and 45 minutes) in the summer exam period.
Case analysis (20%) in the LT.

The group project will consist of a case study developed by the lecturer and presenting a (simplified version of a) real world problem that is amenable to optimisation and simulation techniques that are taught in the course. The students will need to choose the appropriate techniques, develop a mathematical model, implement it using the software taught in the course, and write a report describing the approach and reporting critically the results obtained from the solution of the model. 

The group project will be in randomly allocated groups of at most 3, and students will need to submit a teamwork evaluation form to assess whether the workload was fair and balanced.

Important information in response to COVID-19

Please note that during 2020/21 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.

Key facts

Department: Mathematics

Total students 2019/20: 73

Average class size 2019/20: 14

Capped 2019/20: No

Value: One Unit

Guidelines for interpreting course guide information

Personal development skills

  • Self-management
  • Team working
  • Problem solving
  • Application of information skills
  • Communication
  • Application of numeracy skills
  • Specialist skills