2020/21 Undergraduate Module Catalogue
COMP3778 Decision Modelling
10 creditsClass Size: 60
Module manager: Dr Raymond Kwan
Email: r.s.kwan@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2020/21
This module is not approved as a discovery module
Module summary
This module explores how particular management science models and computational techniques can be used to help decision makers in typical business problem scenarios. For example, this might relate to determining production over the next planning period; financial investments. Software tools will be used to demonstrate how practical solutions can be obtained and analysed. Case studies, extracted from the UK rail industry served by the University of Leeds spin-out Tracsis Plc, connect decision modelling theories with real life; which would reinforce and broaden the student’s business acumen thereby enhancing their employability.Objectives
On completion of this module, students should be able to- understand the importance and relevance of decision models for decision making in a variety of business and industry contexts
- demonstrate awareness of a range of optimisation models, tools and techniques through application
- demonstrate awareness of recent technological advances, trends and challenges in decision analysis through critical review and discussion of relevant literature
- critically evaluate different decision models given a decision setting
- prepare problems for solution using optimisation tools
- build an appropriate mathematical model of a decision problem
- analyse the output of the model for specific problems and perform sensitivity analysis
Syllabus
- Operational Research methodology
- Linear Programming models: general structure and formulation, properties and assumptions, sensitivity analysis
- Integer LP models, binary variables
- Spreadsheet based LP Solver, practical implementation of an LP model, presentation of model and results
- Classes of LP models: product mix, transportation, assignment, network flow
- Minimum spanning tree problems and heuristics
- Decision making under uncertainty; payoff and expected values; value of perfect information
- Decision Trees
- Attitude to risks in decision making; utility theory
- Recent technological advances, trends and challenges in decision analysis
- Case studies: decision modelling in the rail industry
Teaching methods
Delivery type | Number | Length hours | Student hours |
Laboratory | 6 | 1.00 | 6.00 |
Class tests, exams and assessment | 1 | 2.00 | 2.00 |
Lecture | 22 | 1.00 | 22.00 |
Private study hours | 70.00 | ||
Total Contact hours | 30.00 | ||
Total hours (100hr per 10 credits) | 100.00 |
Private study
- Taught session follow-up: 20 hours- Self-directed study: 30 hours
- Assessment activities: 20 hours
Opportunities for Formative Feedback
Attendance and formative assessmentMethods of assessment
Coursework
Assessment type | Notes | % of formal assessment |
In-course Assessment | Practical - MS Excel linear programming model | 25.00 |
In-course Assessment | Practical - MS Excel linear programming model | 25.00 |
In-course Assessment | Written models and exercises | 25.00 |
In-course Assessment | Written models and exercises | 25.00 |
Total percentage (Assessment Coursework) | 100.00 |
This module will be reassessed by an online time-constrained assessment.
Reading list
The reading list is available from the Library websiteLast updated: 18/09/2020 12:25:42
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