Prescriptive Analytics III: Optimisation

Module overview

Organisations are typically faced with many decision problems in the running of their operations and they strive to make better decisions by finding good, or ideally the best (optimal), solutions to such problems. This module is concerned with how decision problems can be formulated mathematically and solved optimally to support the decision making process in organisations. The module will introduce several optimisation techniques and illustrate the application of these techniques on problems from different types of industries.

The techniques introduced in this module have a wide range of applicability on decision problems arising in, among others, resource and workforce planning, business investment, machine scheduling, logistics, and supply chain management. The underlying methods of optimisation studied in this module, however, are generally applicable and not restricted to prescriptive analytics.

Aims and Objectives

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

apply the appropriate optimisation techniques to model a wide range of business problems;
analyse results and interpret outcome;
recognise and define an optimisation problem;
perform sensitivity analysis and interpret it.

Transferable and Generic Skills

Having successfully completed this module you will be able to:

teamwork.
learning skills;
problem-solve;
computer skills;
numeracy;

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

the principles and concepts of optimisation;
the core optimisation techniques and approaches used.

Learning and Teaching

Teaching and learning methods

The module is taught through a combination of lectures (two per week for twelve weeks), classes, computer lab sessions, case studies and problem sheets

Study time
Type	Hours
Completion of assessment task	36
Revision	34
Lecture	24
Wider reading or practice	10
Tutorial	10
Preparation for scheduled sessions	24
Follow-up work	12
Total study time	150

Resources & Reading list

Textbooks

Hillier, F.S. and Lieberman, G.J. (2010). Introduction to Operations Research. McGraw Hill.

Winston, W. L. (1998). Operations Research: Applications and Algorithms (4th ed.). Brooks/Cole.

Rardin, L.R. (1998). Optimization in Operations Research. Prentice Hall.

Assessment

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Coursework Examination In-class activities

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Examination	80%
Group Coursework	20%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Examination	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Coursework	20%
Examination	80%

Repeat Information

Repeat type: Internal & External