Postgraduate Course: Optimisation Models for Marketing Decisions (CMSE11473)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course aims to provide the tools for modelling and solving optimisation models arriving in business contexts. Optimisation models frequently appear in business contexts when a manager faces complex decision making. The course will present the students with a variety of quantitative skills that can assist a decision maker in modelling, understanding and finding optimal (close to optimal) solutions to a business problem.
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| Course description |
Academic Description:
This course aims to provide the tools for modelling and solving optimisation models arriving in business contexts. Optimisation models frequently appear in business contexts when a manager faces complex decision making. The course will present the students with a variety of quantitative skills that can assist a decision maker in modelling, understanding and finding solutions to a business problem.
Spreadsheet modelling using Microsoft Excel will also be covered at the outset and intensively practised during the course. The course focus is practical, with abundance of hands-on, close-to-reality, case studies. The students will develop the ability of understand the business context, decide on the complexity of the model that fits the problem better, use mathematical modelling and reasoning to construct the model, implement the model in the spreadsheet and decide on the type of optimisation algorithm that better suits the model. At the end of the course, the student will also be in a position to correctly interpret and implement the solution provided by the model and analyse its sensitivity to changes in the data.
Outline Content:
A selection of topics among:
- Introduction to operational research
- Linear programming
- Integer programming
- Goal programming
- Nonlinear unconstrained optimisation
- Metaheuristics
- Heuristics in logistics
Student Learning Experience:
The course focus is practical, with abundance of hands-on, close-to-reality, case studies. Spreadsheet modelling using Microsoft Excel will also be covered at the outset and intensively practiced during the course.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
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 |
Block 3 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 10,
Seminar/Tutorial Hours 5,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
83 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
50% coursework (individual) - assesses all course Learning Outcomes
50% coursework (group) - assesses all course Learning Outcomes |
| Feedback |
Formative feedback: Students will gain feedback on their understanding of the material when they perform computer lab exercises. Students may ask questions in lectures and in forums to assess their knowledge.
Summative assessment: Individual and group coursework. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand and critically discuss the nature of problem solving and decision making (and its support through quantitative techniques) in Business Analytics.
- Understand and critically assess various methods and algorithms for finding optimal solutions.
- Understand and apply the techniques for modelling optimisation problems.
- Understand and critically assess the relative merits of the techniques for modelling optimisation problems.
- Work within pairs in modelling optimisation problems, discussing different modelling options and deciding what level of complexity suits each business case.
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Learning Resources
Operations Research. Taha, Hamdy A.10th edition.Harlow, United KingdomPearson Education Limited, 2016
Anderson, D.R., Sweeney, D, Williamns, A.W. and Wisniewski, M. (2017), An Introduction to Management Science - Quantitative Approaches to Decision Making (Third Edition), South-Western Cengage Learning
Albright, S.C and Winston, W.L. (2004), Spreadsheet Modeling and Applications - Essentials of Practical Management Science, South-Western Cengage Learning
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Additional Information
| Graduate Attributes and Skills |
Cognitive Skills
Students will develop skills such as:
- the ability to build models to support management decision making;
- the ability to critically validate models for management decision making;
- the ability to interpret results and suggest best solutions for decision making models;
- the ability to understand the intuition behind optimisation algorithms.
Subject Specific Skills
Students will gain:
- an appreciation of methods involved in business decision modelling;
- experience in applying model building methods to realistic examples involving optimisation;
- experience in using spreadsheets to tackle optimisation problems.
By the end of the course students will be expected to:
- be able to plan and carry out analyses based on construction and solution of appropriate optimisation models;
- be able to employ analytical and problem solving skills;
- show that they can report results in a concise way;
- have enhanced their skills in using commercial software products.
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| Keywords | Not entered |
Contacts
| Course organiser | Dr Belén Martín-Barragán
Tel: (0131 6)51 5539
Email: |
Course secretary | Ms Emily Davis
Tel: (0131 6)51 7112
Email: |
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