Introductory description

This is a short intensive module on theoretical and practical aspects of linear programming, including an introduction to other optimization techniques. This module will be run in the first five weeks of Term 3. The module is a prerequisite for IB2070 Mathematical Programming II.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Introduction to Operational Research
Introduction to Linear Programming
Introduction to basic algorithms for solving linear programming problems
Practical computer work using a Linear Programming computer package
Formulation methods and Interpretation of solutions
Distribution / transportation models
Introduction to Game Theory

Learning outcomes

By the end of the module, students should be able to:

• Recognise, formulate and solve business optimisation problems.
• List and challenge the assumptions underpinning each of the key models studied.
• Reflect critically on the limitations of each of the models studied.
• Report on the meaning of the optimal solutions in a manner suited to a business context.

Indicative reading list

D. R. Anderson, D. J. Sweeney, T. A. Williams, J. D. Camm and J. J. Cochran (2015). An Introduction to Management Science: Quantitative Approaches to Decision Making. Cengage Learning.

Bynum, M.L. (2022) Pyomo - optimization modeling in Python. Third edition. Cham, Switzerland: Springer.

Interdisciplinary

Core module for key interdisciplinary degree (MORSE).

Subject specific skills

Analytically solve linear optimisation problems.

Transferable skills

Model a business optimisation problem in a suitable mathematical form and interpret optimal mathematical solutions in the business context.