Timetable information in the Course Catalogue may be subject to change.

University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : Business School : Business Studies

Undergraduate Course: Mathematical Programming in Advanced Analytics (BUST10134)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Management Science and Operations Analytics (BUST10135) OR Business Analytics and Information Systems (BUST08032)
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesVisiting students must have 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.
High Demand Course? Yes
Course Delivery Information
Academic year 2024/25, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 20, Seminar/Tutorial Hours 10, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 166 )
Assessment (Further Info) Written Exam 0 %, Coursework 90 %, Practical Exam 10 %
Additional Information (Assessment) 15% Project (Group) - Incudes 25% peer review

35% Project (Group) - Includes 25% peer review

10% Presentation (Group)

40% Coursework (Individual)
Feedback Formative: Feedback will be provided throughout the course.

Summative: Feedback will be provided on the assessments within agreed deadlines.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Assess critically the utility of a number of mathematical programming techniques.
  2. Describe mathematical programming solution strategies and techniques.
  3. Use mathematical programming methods to address management decision problems.
Reading List
Recommended Reading:
1. S. P. Bradley, A. C. Hax, and T. L. Magnanti (1977), Applied Mathematical Programming, Addison-Wesley. [JCM Library shelfmark QA402.5 Bra;
2. Williams, H. P. (2013). Model building in mathematical programming. John Wiley & Sons;
3. Birge, J. R., & Louveaux, F. (2011). Introduction to stochastic programming (Springer series in operations research and financial engineering).
Additional Information
Course URL
Graduate Attributes and Skills Communication, ICT, and Numeracy Skills

After completing this course, students should be able to:

Critically evaluate and present digital and other sources, research methods, data and information; discern their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of organisational contexts.

Practice: Applied Knowledge, Skills and Understanding

After completing this course, students should be able to:

Apply creative, innovative, entrepreneurial, sustainable and responsible business solutions to address social, economic and environmental global challenges.

Knowledge and Understanding

After completing this course, students should be able to:

Demonstrate a thorough knowledge and understanding of contemporary organisational disciplines; comprehend the role of business within the contemporary world; and critically evaluate and synthesise primary and secondary research and sources of evidence in order to make, and present, well informed and transparent organisation-related decisions, which have a positive global impact.

Identify, define and analyse theoretical and applied business and management problems, and develop approaches, informed by an understanding of appropriate quantitative and/or qualitative techniques, to explore and solve them responsibly.
Additional Class Delivery Information One x 2-hour lecture in Weeks 1-10; one seminar on Tuesdays, from 14:00-15:30 in Weeks 2-8; 2-hour tutorial on Tuesday in Weeks 9-10.
KeywordsMathematical Programming in Advanced Analytics
Course organiserDr Douglas Alem
Tel: (0131 6)51 1036
Course secretaryMr Ewan Henderson
Help & Information
Search DPTs and Courses
Degree Programmes
Browse DPTs
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Important Information