Introductory description

During the course of the module, a number of lectures on advanced topic areas in OR will be provided with sufficient details in their theory, methodology and applications. Students will be involved in extensive discussions with faculty and fellow students, in addition to lecture exercises and module assignments.

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.

Indicative topics to be covered are recent advances in

• Game theory
• Combinatorial optimisation
• Network problems
• Simulation optimisation
• Metaheuristics
• Scheduling
• Transportation
• Approximate Dynamic Programming
• Revenue management
• Stochastic optimisation
• Multi-objective optimisation
• Problem structuring and OR modelling

Learning outcomes

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

• Have a broad and in-depth understanding of a range of OR subject areas in terms of theory and methodology
• Have an in-depth knowledge of the current trends in some OR areas

Indicative reading list

• Specific chapters of classic OR textbooks (such as W.L. Winston, Operations Research: Applications and Algorithms).
• Relevant influential journal papers
• Most recent relevant research publications

Subject specific skills

• Broad and in-depth understanding of a range of OR subject areas in terms of theory and methodology
• An in-depth knowledge of the current trends in some OR areas
• Mathematical modelling
• Algorithmic solution

Transferable skills

• Model and solution interpretations and communications
• Problem modelling abilities
• Analytical and solution skills
• Implementation skills