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The University of Southampton

MATH3081 Operational Research

Module Overview

The module introduces the operational research approach for modelling and solving engineering and management problems.

Aims and Objectives

Module Aims

The aim of the module is to introduce some of the most widely used operational research techniques and provide an understanding of how they can be used in practice.

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • Build models for simple problems in managerial decision making
  • Demonstrate knowledge and understanding of operational research techniques for simulation, production scheduling, project management, queueing analysis, simulation, inventory control and decision analysis
  • Structure practical problems
  • Develop and run computer simulation models
  • Utilise suitable mathematical methods to solve these models
  • Analyse and solve some managerial problems in engineering with some of the common operational research methods and techniques
  • Demonstrate writing skills


1. Discrete Event Simulation: design of a simulation model and programme, input modelling including random number generation and random variable generation, output analysis, design of simulation experiments, simulation modelling using the Simul8 software. 2. Production Scheduling: Types of scheduling models, various algorithms for single machine scheduling, list scheduling for parallel machine scheduling, Johnson’s algorithm for flow shop scheduling, use of heuristic methods. 3. Project Management: Network representation of engineering projects, Critical Path Method for scheduling a project, project scheduling with limited resources, crashing project completion time. 4. Queuing Theory: dynamics of a queueing system, modelling of some typical basic queues, evaluating average queue length and waiting time. 5. Decision Analysis: pay-off table for one-off decisions and discussion of decision criteria, use of decision trees for more complex environments, decision making based on sampling (with Bayes Theorem used to calculate posterior probabilities). 6. Inventory Models: the Economic Order Quantity model, including sensitivity analysis, economic production lot size model, quantity discount models, Wagner-Whitin model for dynamic demand

Learning and Teaching

Teaching and learning methods

The module will be taught using a combination of lectures and computer workshops.

Independent Study102
Total study time150

Resources & Reading list

M Pidd (2004). Computer Simulation in Management Science.. 

D.R. Anderson, D.J Sweeney, , T.A. Williams (2008). An Introduction to Management Science: Quantitative Approaches to Decision Making. 



MethodPercentage contribution
Coursework assignment(s) 20%
Exam 80%


MethodPercentage contribution
Exam 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Prerequisites: MATH1054 OR MATH1055


Costs associated with this module

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

In addition to this, students registered for this module typically also have to pay for:

Books and Stationery equipment

Course texts are provided by the library and there are no additional compulsory costs associated with the module.

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at

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