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Semester 2

Mathematical Programming in Advanced Analytics (BUST10134)

Course Website

http://www.bus.ed.ac.uk/programmes/ugpc.html

Subject

Business Studies

College

CAHSS

Credits

20

Normal Year Taken

3

Delivery Session Year

2023/2024

Pre-requisites

Visiting students must have completed at least 4 Business courses at grade B or above. This MUST INCLUDE one course equivalent to BUST10135 Management Science and Operations Analytics OR BUST08032 Business Analytics and Information Systems. This course cannot be taken alongside BUST08032 Business Analytics and Information Systems. We will only consider University/College level courses.

Course Summary

This course will provide students with the foundations of prescriptive analytics with emphasis on mathematical programming concepts, applications, models, and solution methods.

Course Description

Optimisation problems are concerned with optimising an objective function subject to a set of constraints. When optimisation problems are translated in algebraic form, we refer to them as mathematical programs. Mathematical programming, as an area within Operational Research (OR), Management Science (MS) and Business Analytics (BA), is concerned with model building and strategies and methods for solving mathematical programs. In this course, we address model building in OR/MS/BA, present a variety of typical OR/MS/BA problems and their mathematical programming formulations, provide general tips on how to model managerial situations, and discuss solution strategies for a class of deterministic and/or under uncertainty problems. Last, but not least, students will learn how to use/build prescriptive analytics tools in the context of decision problems faced by business managers. The four main topics covered in this course are: Outline Conent 1. Introduction to OR/MS and Model Building; 2. Linear Programming (LP): Review of basic concepts and methods; namely, the simplex method and the dual simplex method, sensitivity analysis, and duality theory; 3. Integer Programming (IP): Basic concepts, relationship with linear programming, strategies and methods of solving integer programs; namely, brand-and-bound algorithms, cutting plane algorithms, and brand-and-cut algorithms; 4. Optimisation under Uncertainty: Basic concepts in two-stage stochastic programming and robust optimisation, relationship with deterministic equivalent formulations, and applications. Student Learning Experiences This lecture and tutorial programme, which builds on knowledge from Management Science & Business Analytics courses in earlier years, develops mathematical programming model building and solution techniques, and is supported by mandatory readings and supervised discussion sessions. These supervised sessions aim at discussing how to put into practice the concepts and methods presented in the lectures and learned from the mandatory readings and the term projects. In addition, these sessions also serve as advice/support sessions so that students can seek feedback on their term projects work-in-progress. The student experience requires active learning and engagement, which requires students to read relevant chapters in the textbooks and other sources before attending classes. Students are required to complete three group projects using GAMS. Besides attending lectures and supervised discussion sessions (both of which are compulsory), students will work in groups on realistic projects (groups will be formed by the lecturer to reflect a heterogeneity of skills required for the projects) and present their work in class to an audience that may include practitioners and term project providers. Guest speakers might be invited for the benefit of students, however, students should not expect any hand-outs from the guests.

Assessment Information

Written Exam 0%, Coursework 90%, Practical Exam 10%

view the timetable and further details for this course