electiveHQ

With the availability of data and computing power, analytical and mathematical approaches have become increasingly important in addressing business, engineering and other problems; notably decision problems where a decision must be made subject to constraints such as limitations on resources; or data analytics problems where we seek useful information in a large dataset. This course introduces the concept of Mathematical and Analytics Modelling in Decision Problems, and surveys some of the major mathematical models and solution techniques. Participants will learn how to conceptualise complex business problems and transform them into a set of equations (models) that describe the problem. For example a network model, when we are given several distinct points (such as cities) and links connecting them (such as a road network), may be used to determine the optimal routing of goods in a supply chain. We emphasise how to correctly formulate a mathematical model (given a real-world problem description), and the trade-offs necessary between comprehensiveness and usability. Business problems can be modelled and solved using optimisation techniques such and Linear or (Mixed) Integer Programming. Participants will be introduced to the use of computer packages to implement models for sample problems and assignments. This module will have Face-To-Face delivery with lecture recordings made available online for those who cannot attend. The principles of Active Learning guide the face-to-face contact sessions, with students engaging in hands-on mathematical modelling exercises. Self-assessment exercises and additional online resources on Brightspace will complement the face-to-face sessions. The assessments will be a mix of individual (MCQ) and group (problem modelling and solution interpretation) to provide opportunities for both individual deep engagement, and to improve teamwork and report writing skills.