Operational Research and Systems Analysis

Module aims


To introduce the students to some of the operational research methods that are used in the systems approach to Engineering and Management, so as to provide them with the requisite tools for the mathematical representation of decision-making problems, in particular emphasising the roles of uncertainty and risk.
 

Learning outcomes

On successfully completing this course unit, students will be able to:

  •     Use standard dynamic programming techniques.
  •     Represent a system using Markov Chains.
  •     Have an understanding of how operational research techniques can be applied to engineering decision-making (resource allocation; production scheduling; environmental risk minimisation; transport planning).
  •     Ability to use software to quickly prototype mathematical programming models relating to engineering problems.
  •     Utilise skills in optimisation, decision-making, and stochastic methods.
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Module syllabus

No.

Topic

Staff

01

Dynamic Programming

CO

02

Dynamic Programming

CO

03

Probabilistic Dynamic Programming

CO

04

Markov Chains

CO

05

Markov Chains

CO

06

Decision Theory

CO

07

Decision Theory

CO

08

Solution of Linear Programmes I

PA

09

Solution of Linear Programmes II

PA

10

Metaheuristic Solution Techniques

PA

  • Dynamic programming with application to: minimal cost path problem; allocation of resources; production scheduling; replacement policy.
  • Markov Chain Methods including: probability transition matrix; steady-state; application to project management; water resource systems.
  • Bayesian decision-making including: criteria for decision-making under uncertainty; estimation of the value of information; applications to management and optimal use of resources.
  • Introduction to discrete optimisation including: overview of mathematical programming; workshop on use of mathematical programming software.

Reading list

Module leaders

Dr Christian Onof