Postgraduate Course: Business Analytics with Heuristics (CMSE11355)
Course Outline
| School | Business School |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 15 |
ECTS Credits | 7.5 |
| Summary | This is a compulsory course for the MSc in Business Analytics programme. The course will provide students with the methodologies and methods concerned with the design and implementation of heuristics, metaheuristics, and hyperheuristics with applications in transport analytics and fleet management. |
| Course description |
In many real life situations, a large number of decision problems have to be solved very frequently and within an acceptable period of time to allow for the implementation of decisions. From a computational perspective, optimal analysis and solution methodologies are too time-consuming to be acceptable. This course offers alternatives to optimal analysis and solution methodologies for complex and realistic size problems; namely, heuristics. In sum, this course aims at training students in the field of heuristics to respond to the job market needs using a variety of methodologies and methods to address decision making problems in business and covers the design of heuristics, metaheuristics, and hyperheuristics. Applications mainly focus on transport analytics and fleet management, not only as a relevant application area but also because of the importance of transport in the economy and the increasing challenges facing transport, logistics and fleet managers.
The objective of this course is to enhance students¿ understanding of the critical nature of designing and/or selecting appropriate methodologies and methods for solving complex problems of large sizes and the role such methods play in decision support systems and their implementation. The course also aims at training students to critically assess heuristic modelling and solution methodologies. In addition, students will learn how to design and implement state-of-the-art analytics with heuristics tools to quickly devise good quality solutions to complex decision problems faced by business managers. In terms of application areas for the methodologies and methods presented in the lectures, the focus shall mainly be on transport analytics and fleet management. 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 heuristic analytics, and to present their work to the rest of the class and to an external panel when the projects are supplied by industry.
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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 2017/18, 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:
150
(
Lecture Hours 20,
Seminar/Tutorial Hours 10,
Programme Level Learning and Teaching Hours 3,
Directed Learning and Independent Learning Hours
117 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
90 %,
Practical Exam
10 %
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| Additional Information (Assessment) |
Examination
Final exam 30% weighting
Coursework
Term projects 60% weighting
Presentations 10% weighting
- Term projects (60% of the mark including a peer assessment component worth 10%) in which students will have to undertake the design and implementation of a decision support system (DSS) to assist with decision making in a given application area in business.
- Presentations (10% of the final mark) involving a demonstration of the DSS and justification of its design choices to demonstrate their ability to design DSSs to address real world problems and to convince their line managers or sponsors to adopt the proposed DSS
- Exam(s) (30% of the final mark) |
| 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 heuristic analytics using the proper terminology
- Identify and properly state decision problems in different business settings, and model and solve them within one or several heuristic frameworks covered in the course
- Interpret results/solutions in light of the possible courses of action for a given business problem or situation, formulate managerial guidelines and make recommendations
- Critically discuss alternative heuristic analytics approaches and methods
- Work in groups to create viable heuristic solutions to decision making problems and communicate such solutions effectively and efficiently to a critical audience of non-specialists
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Reading List
SUGGESTIONS:
E.L. Lawler, D.B. Shmoys, A.H.G. Rinnooy Kan, J.K. Lenstra (1985), The Traveling Salesman Problem, John Wiley & Sons
Gutin G. and Punnen A.P. (2007), The Traveling Salesman Problem and its Variations, Springer Science and Business Media
Toth P. and Vigo D. (2002), The Vehicle Routing Problem, SIAM monographs on Discrete Mathematics and Applications, Society for Industrial and Applied Mathematics
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | BA-BAH |
Contacts
| Course organiser | Prof Jamal Ouenniche
Tel: (0131 6)50 3792
Email: |
Course secretary | Mr Peter Newcombe
Tel: (0131 6)51 3013
Email: |
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