Operational Research has been used intensively in business, industry and government. Many new analytical methods have evolved, such as: mathematical programming, simulation, game theory, queuing theory, network analysis, decision analysis, multicriteria analysis, etc., which have powerful application to practical problems with the appropriate logical structure.

Operational Research in practice is a team effort, requiring close cooperation among the decision-makers, the skilled OR analyst and the people who will be affected by the management action.

The OR Society of United Kingdom, the world's oldest established society catering for the needs of the OR profession considers that Operational Research looks at an organisation's operations - the functions it exists to perform. The objective of Operational Researchers is to work with clients to find practical and pragmatic solutions to operational or strategic problems, often working within tight timing constraints. Once a good or better way of proceeding has been identified, Operational Researchers can also be central to the management of implementing the proposed changes.

Organisations may seek a very wide range of operational improvements - for example, greater efficiency, better customer service, higher quality or lower cost. Whatever the business engineering aim, OR can offer the flexibility and adaptability to provide objective help.

Most of the problems OR tackles are messy and complex, often entailing considerable uncertainty. OR can use advanced quantitative methods, modelling, problem structuring, simulation and other analytical techniques to examine assumptions, facilitate an in -depth understanding and decide on practical action.

 

The purpose of this course is to study the basic tools for quantitative methods for decision making. The emphasis is on solution methods and strategies.

Learning outcomes:

The student will understand the underlying algorithms and be able to interpret the results. He/She will be able to formulate problems as abstract models which can be solved by generic algorithms.

Syllabus:

To introduce optimal decision making processes in design and management. To give the necessary mathematical background and its application to solving a selection of constrained optimisation problems with special reference to computation.

Preview: optimal policy in design and management: mathematical models.

Linear programming: The Simplex method, two-phase Simplex method, duality, shadow prices.

Linear integer programming: Gomory's cutting plane methods for pure and mixed linear integer programming. Search methods; branch and bound algorithms.

Game theory: two person non-co-operative games. Saddle points. Matrix games.