The University of Southampton

MATH6004 Stochastic OR Methods

Module Overview

The Stochastic OR Techniques part introduces the concepts and applications of the following four topics: queuing systems, inventory systems, reliability theory and decision theory. Models and examples are also given to demonstrate applications of the topics. Discrete event simulation is taught separately via lectures and computer workshops.

Aims and Objectives

Module Aims

• Provide the students with a grounding in the stochastic elements of operations research. • Understand how to implement and analyse a discrete event simulation model

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • Having successfully completed this module, you will have knowledge and understanding of the concepts and applications of the following five topics: queuing systems, inventory systems, reliability theory, decision theory and simulation.
  • You will have gained skills in technical report writing.
  • Having successfully completed this module, you will have practical skills in decision analysis, modelling queueing and inventory systems and calculating the reliability of systems.
  • Having successfully completed this module, you will have gained skills in team working.
  • You will understand how to implement and analyse a discrete event simulation model using Simul8 software.
  • You will have gained skills in using computer software and programming.


Queuing Systems: Basic elements of a queue, continuous time Markov process, exponential distribution, and applications of queuing systems. Inventory System: EOQ models, newsboy models, inventory model with stochastic demand. Reliability Theory: Reliability of a component, reliability of a system, maintenance models, reliability and economics. Decision Theory: Bayes’ rule, value of information, Decision trees, and utility. Simulation: The emphasis is on simulation computing skills, which are developed in computer labs based on the SIMUL8 package. Lectures cover: the concept of randomness; generation of random numbers; and sampling from probability distributions. The Modelling Process: Activity flow diagrams, validation and verification, experimental design, analysis and interpretation of results..

Learning and Teaching

Teaching and learning methods

Twelve 2-hour OR techniques lectures Twelve 1-hour OR techniques tutorial sessions Three 2-hour simulation lectures Six 1-hour simulation computer sessions

Independent Study96
Total study time150

Resources & Reading list

Nelson, BL. Stochastic Modeling: Analysis & Simulation. 

Schaum. Outline of Operations Research. 

Winston, WL. Operations Research: Applications and Algorithms. 

Hillier, F. Introduction to Operations Research. 

Ross, SM. Applied Probability Models with Optimization Applications. 


Assessment Strategy

The repeat assessment includes both coursework and examination. If the coursework was previously passed, it is not retaken.


MethodPercentage contribution
Closed book Examination  (2 hours) 70%
Coursework 30%


MethodPercentage contribution
Closed book Examination 70%
Coursework 30%

Repeat Information

Repeat type: Internal & External

Linked modules

One of the pre-requisites for MATH6013


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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