Undergraduate Course: Mathematical Programming in Advanced Analytics (BUST10134)
Course Outline
School  Business School 
College  College of Arts, Humanities and Social Sciences 
Credit level (Normal year taken)  SCQF Level 10 (Year 3 Undergraduate) 
Availability  Available to all students 
SCQF Credits  20 
ECTS Credits  10 
Summary  This course will provide students with the foundations of prescriptive analytics with emphasis on mathematical programming concepts, applications, models, and solution methods. (This course was formerly entitled Mathematical Programming BUST10011.) 
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:
Syllabus
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, brandandbound algorithms, cutting plane algorithms, and brandandcut algorithms;
4. Optimisation under Uncertainty: Basic concepts in twostage stochastic programming and robust optimisation, relationship with deterministic equivalent formulations, and applications.
PLANNED 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 workinprogress. 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 handouts from the guests.

Information for Visiting Students
Prerequisites  Visiting 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 2019/20, 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) 
There will be a group project on prescriptive business analytics (90%) covering different aspects of the course. The project will be divided into Part 1, Part 2 and Part 3, with each part increasing in difficulty, building on understanding and technical skills. The aim of the project is to allow students to 'learn by doing' and to enhance their skills in using stateoftheart prescriptive analytics tools in the context of decision problems faced by business managers. For each part of the group project, students are expected to produce reports with both academic rigour and managerial insight. The suggested template for the reports will be available on Learn.
Each part of the project involves the writing of a report in which students are expected to explain/document the mathematical formulation of the problem under consideration, explain/document the solution method chosen or proposed in a way that is accessible to both technical and nontechnical audiences, interpret solutions, formulate managerial guidelines, and make recommendations.
There will be a Workshop Oral Presentation in which the groups are supposed to present their final project. It involves two parts: (i) the poster board containing the problem design/solution and analyses; and (ii) the presentation itself, including a peer assessment component. The primary goal of the oral presentation through the poster board is to encourage students to communicate their term projects for an audience consisting of both experts and nonexperts. The workshop will also stimulate students to know each other's approach for the same (or similar) problem, thus engaging colleagues in a dialogue about the work. The poster must contain the problem contextualization, the optimisation model, the solutions, and a brief conclusion on managerial implications and insights. In the end, it is expected that the students convince their line managers or sponsors to implement the proposed solutions. The poster must be designed according to the required template (it will be available on Learn). 
Feedback 
Feedback will be given on the preceding project before the next one is due to be submitted. 
No Exam Information 
Learning Outcomes
On completion of this course, the student will be able to:
 Assess critically the utility of a number of mathematical programming techniques.
 Describe mathematical programming solution strategies and techniques.
 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, AddisonWesley. [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 
http://www.bus.ed.ac.uk/programmes/ugpc.html 
Graduate Attributes and Skills 
Cognitive Skills
On completion of the course students should:
(i) demonstrate ability in deciding whether a problem is amenable to solution by mathematical programming techniques;
(ii) demonstrate ability in using mathematical programming solution techniques;
(iii) demonstrate ability in explaining the solution to mathematical programming models.
Key Skills
On completion of the course students should:
(i) be able to formulate problems in mathematical programming terms;
(ii) be able to solve mathematical programming problems using commercial software;
(iii) be able to communicate mathematical programming solutions to nonspecialists.
Subject Specific Skills
On completion of the course students should:
(i) have extended their model building skills;
(ii) have increased their model solution skills. 
Additional Class Delivery Information 
One x 2hour lecture/discussion on Mondays at 16.1018.00 in Weeks 110; 2hour tutorials on Thursdays at 16.1018.00 in Weeks 3, 4, 6, 7 and 9. 
Keywords  Mathematical Programming in Advanced Analytics 
Contacts
Course organiser  Dr Douglas Alem
Tel: (0131 6)51 1036
Email: douglas.alem@ed.ac.uk 
Course secretary  Ms Patricia WardScaltsas
Tel: (0131 6)50 3823
Email: Patricia.WardScaltsas@ed.ac.uk 

