Postgraduate Course: Prescriptive Analytics with Mathematical Programming (CMSE11431)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course provides students with the fundamentals of linear and integer optimisation to model and analyse real-world business applications.
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| 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 lectures. In 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.
Tutorial/seminar hours represent the minimum total live hours - online or in-person - a student can expect to receive on this course. These hours may be delivered in tutorial/seminar, lecture, workshop or other interactive whole class or small group format. These live hours may be supplemented by pre-recorded lecture material for students to engage with asynchronously.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | For MSc Business Analytics students, or by permission of course organiser. Please contact the course secretary. |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2022/23, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Seminar/Tutorial Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
176 )
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| Additional Information (Learning and Teaching) |
Seminar/Tutorial hrs are the min total live hrs, online or in-person, students can expect to receive
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
60% coursework (individual) - assesses course Learning Outcomes 1, 2, 4
40% coursework (group) - assesses course Learning Outcomes 3, 4, 5 |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Discuss the concept and methods of prescriptive analytics, in general, and mathematical programming, in particular, using the proper terminology.
- 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
- Interpret solutions, formulate managerial guidelines and make recommendations.
- Critically discuss alternative prescriptive analytics approaches and methods.
- Communicate solutions effectively and efficiently to a critical audience of non-specialists.
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Reading List
-H.P. Williams (2013). Model Building in Mathematical Programming, fifth edition, Wiley.
-Bertsimas, D., & Tsitsiklis, J. N. (1997). Introduction to linear optimization. Belmont, MA: Athena Scientific.
-Chen, D. S., Batson, R. G., & Dang, Y. (2011). Applied integer programming: modeling and solution. John Wiley & Sons.
-S. P. Bradley, A. C. Hax, and T. L. Magnanti (1977). Applied Mathematical Programming, Addison-Wesley.
Resource List:
https://eu01.alma.exlibrisgroup.com/leganto/public/44UOE_INST/lists/26181400940002466?auth=SAML |
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.
-Develop appropriate programming skills for business analysis.
Transferable skills
-Report writing.
-Quantitative skills.
-Self-awareness through written reflection. |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Nader Azizi
Tel: (0131 6)51 1491
Email: |
Course secretary | Ms Emily Davis
Tel: (0131 6)51 7112
Email: |
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