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DRPS : Course Catalogue : Business School : Common Courses (Management School)

Postgraduate Course: Prescriptive Analytics with Mathematical Programming (CMSE11431)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThis course provides students with the fundamentals of linear and integer optimisation to model and analyse real-world business applications.
Course description Academic 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 and present solution methods for linear and integer programs. The objective of this course is to enhance students' understanding of the critical nature of building appropriate mathematical models as simplified representations of realistic managerial situations, and the role such models play in prescribing solutions to decision making problems. The course also aims at training students to critically assess mathematical programming models and solution methodologies. In addition, students will learn how to use state-of-the-art prescriptive analytics tools in the context of decision problems faced by business managers. The course provides opportunities for students to learn from each other, from practitioners in the field, and from the latest theoretical and applied research in the field. The course will require students to work in groups on realistic projects in different business settings involving prescriptive analytics, and to present their work to the rest of the class and to an external panel when the projects are supplied by industry.

Outline Content: The course is organised around the following three main teaching blocks:
Block 1: Introduction to OR/MS/BA, typical methodological steps of an OR/MS/BA study, and model building with applications in business decision making.
Block 2: Linear programming (LP) - Review of basic concepts and methods; namely, the simplex method, sensitivity analysis, and duality theory with applications in business decision making.
Block 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, with applications in business decision making.

Student Learning Experience
Students are expected to learn basic concepts and theories from 10 two-hour lectures for 10 weeks. In 5 two-hour tutorial sessions, they will learn how to apply the basic concepts and theories learned in the lectures, as well as how to use optimisation solvers to address practical problems.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements For MSc Business Analytics students, or by permission of course organiser. Please contact the course secretary.
Course Delivery Information
Academic year 2019/20, Not available to visiting students (SS1) Quota:  47
Course Start Semester 1
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 95 %, Practical Exam 5 %
Additional Information (Assessment) Take-home exam (50%), LO1, LO2, LO3
Group Report 40% - LO2, LO3, LO4.
Group Presentation 10% - LO1, LO3, LO5.

Group Report (40% of the final mark) consists of a project in which students will have to undertake a prescriptive analytics exercise including the following steps:
(1) problem statement,
(2) optimisation model building,
(3) solution design,
(4) data gathering/analysis,
(5) implementation in GAMS/CPLEX,
(6) report on findings in an easy way to experts and non-experts,
(7) formulation of recommendations, and managerial guidelines.

Group Presentation (10% of the final mark) involves two parts:
(i) the poster board containing the problem design/solution that each group will address (5% of the mark);
(ii) the presentation itself (5% of the mark)

Take Home Exam (50% of the final mark) consists of an online exam containing theoretical and practical questions about the content delivered along the course.
Feedback The assessments will be marked according to the University common marking scheme. Feedback on formative assessed work will be provided in line with the Taught Assessment Regulation turnaround period, or in time to be of use in subsequent assessments within the course, whichever is sooner. Summative marks will be returned on a published timetable, which will be communicated to students during semester.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Discuss the concept and methods of prescriptive analytics, in general, and mathematical programming, in particular, using the proper terminology.
  2. Identify and properly state prescriptive analytics optimisation problems in different business settings, model them, choose the right solution methodology and methods and solve them using mathematical programming techniques
  3. Interpret solutions, formulate managerial guidelines and make recommendations.
  4. Critically discuss alternative prescriptive analytics approaches and methods.
  5. Communicate solutions effectively and efficiently to a critical audience of non-specialists.
Reading List
Introduction to Statistical Learning: with Applications in R (Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani)
Additional Information
Graduate Attributes and Skills After completing this course, students should be able to:

Academic skills
-Understand and describe decision/optimisation problems in different business settings.
-Discuss the main concepts and methods applied to mathematical programming.
-Model and solve given problems using the mathematical programming tools covered in the course.
Interpret results/solutions in light of the possible courses of action for a given business problem or situation.
-Select the most suitable mathematical programming technique for a given problem.
-Formulate managerial guidelines and make recommendations.

Intellectual skills
-Identify typical and new problems in different business settings.
-Discuss and apply existing mathematical programming techniques.
-Discuss advantages and limitations of mathematical programming techniques applies to real-world problems.

Professional/ practical skills
-Use state-of-the-art mathematical programming tools in conducting business analysis.
-Use the proper language to communicate solutions from mathematical programming approaches for both experts and non-experts audiences.
-Learn cooperating in teams to conduct practical business analysis.
-Develop appropriate programming skills for business analysis.

Transferable skills
-Report writing and presentation skills.
-Quantitative skills.
-Cooperation skills.
-Self-awareness through written reflection.
KeywordsNot entered
Course organiserDr Douglas Alem
Tel: (0131 6)51 1036
Course secretaryMiss Lauren Millson
Tel: (0131 6)51 3013
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