MA424      Half Unit
Modelling in Operations Research

This information is for the 2021/22 session.

Teacher responsible

Dr Katerina Papadaki and Dr Grammateia Kotsialou


This course is compulsory on the MSc in Operations Research & Analytics. This course is available on the Global MSc in Management, Global MSc in Management (CEMS MiM), Global MSc in Management (MBA Exchange) and MSc in Data Science. This course is available with permission as an outside option to students on other programmes where regulations permit.


Students must know basics of linear algebra (matrix multiplication, geometric interpretation of vectors), linear programming, and probability theory (expected value, conditional probability, independence of random events). For students in the MSc in Operations Research & Analytics, MA423 and ST447 more than cover the prerequisites.

Course content

The course will be in 2 parts, covering the two most prominent tools in operational research: mathematical optimisation, the application of sophisticated mathematical methods to make optimal decisions, and simulation, the playing-out of real-life scenarios in a (computer-based) modelling environment.

Optimisation: This part enables students to formulate, model and solve real-life management problems as Mathematical Optimisation problems. In providing an overview of the most relevant techniques of the field, it teaches a range of approaches to building Mathematical Optimisation models and shows how to solve them and analyse their solutions. Topics include: formulation of management problems using linear and network models; solution of such problems with a special-purpose programming language; interpretation of the solutions; and formulation and solution of nonlinear models including some or all of binary, integer, convex and stochastic programming models.


Simulation: This part develops simulation modelling skills, understanding of the theoretical basis which underpins the simulation methodology, and an appreciation of practical issues in managing a simulation modelling project. Topics include: generating discrete and continuous random variables; Monte Carlo simulation; discrete event simulation; variance reduction techniques; Markov Chain Monte Carlo methods. The course will teach students how to use a simulation modelling software package.


This course is delivered through a combination of seminars and lectures totalling a minimum of 30 hours across MichaelmasTerm. Depending on circumstances, seminars might be online.

Further, there is a minimum of 6 hours of computer workshop sessions delivered in Michaelmas Term. Computer workshops are not mandatory.

Formative coursework

Students will be expected to produce 2 projects in the MT.

Two mock projects will be given to students that resemble the summative projects. Students are asked to submit only selected parts of the mock projects for feedback.

Indicative reading

The reading will be a combination of lecture slides and chapters from the following list of books.


  • W L Winston, Operations Research: Applications and Algorithms, Brooks/Cole (4th ed., 1998)
  • D Bertsimas and J N Tsitsiklis, Introduction to Linear Optimization, Athena Scientific (3rd ed., 1997)
  • George B. Dantzig and Mukund N. Thapa, Linear Programming 2: Theory and extensions, Springer (2003)


  • S Ross, Simulation, Academic Press (5th ed., 2012)
  • Joseph K. Blitzstein, Jessica Hwang, Introduction to Probability, Chapman and Hall/CRC Press (2014)


Project (50%) and project (50%) in the LT.

There will be one project on Mathematical Optimisation and another on Simulation.  The deliverable is a report along with a soft copy of any computer code and solver output.


Course selection videos

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.

Important information in response to COVID-19

Please note that during 2021/22 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 differing needs of students in attendance on campus and those who might be studying online. For example, this may involve changes to the mode of teaching 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 2020/21: 38

Average class size 2020/21: 20

Controlled access 2020/21: No

Value: Half 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