Introductory description

During the course of the module, a number of lectures on advanced topic areas in OR & Analytics 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.

This module aims to support doctoral students in operational research & analytics. Multiple faculty members will participate in the module to ensure students are exposed to a range of advanced topics.

Faculty members will each give lectures in a topic area of OR & Analytics, covering details of theory, methodology and applications.

Students will have an opportunity to make a presentation on some specific topic(s) in OR & Analytics, which will involve modelling, detailed solutions and implementation with their reflections.

Faculty members will provide feedback on students' presentations and their assignments.Indicative topics to be covered are recent advances in:
Data, Software, and Computation Decision Analysis
Energy and Environment
Financial Engineering
Machine Learning and Data Science
Markets, Platforms, and Revenue Management
Operations and Supply Chains
Optimization
Real-World OR Innovations
Security and Defence
Simulations
Societal Impact
Stochastic Models
Transportation

Learning outcomes

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

• Use OR & Analytics modelling techniques to describe a problem
• Understand the strengths and limitations of a variety of OR & Analytics methodologies
• Identify suitable OR & Analytics methodologies for a variety of problem settings
• Apply a range of OR & Analytics methodologies to a given problem using software
• Have a broad and in-depth understanding of a range of OR & Analytics subject areas in terms of theory and methodology
• Have an in-depth knowledge of the current trends in some OR & Analytics areas

Indicative reading list

W.L. Winston (any edition). Operations Research: Applications and Algorithms.
F. Provost and T. Fawcett (2013). Data Science for Business. Sebastopol, Calif.: O’Reilly.
C.M. Bishop (2006). Pattern recognition and machine learning. New York: Springer
D. Simchi-Levi, P.M. Kaminsky, and E. Simchi-Levi (2012). Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies. 3/E.

Subject specific skills

• Use OR & Analytics modelling techniques to describe a problem
• Understand the strengths and limitations of a variety of OR & Analytics methodologies
• Identify suitable OR & Analytics methodologies for a variety of problem settings
• Apply a range of OR & Analytics methodologies to a given problem using software
• Broad and in-depth understanding of a range of OR & Analytics subject areas in terms of theory and methodology
• An in-depth knowledge of the current trends in some OR & Analytics areas

Transferable skills

• Problem modelling abilities
• Analytical and solution skills
• Implementation skills