The aim of the module is to give students hands-on experience in using industry-standard simulation modelling software in order to structure and solve complex and large-scale managerial decision problems.
The module will cover the following indicative topics.
• Queuing theory: Students will be introduced to the basic underpinnings of queuing theory, including key assumptions, benefits, and limitations.
• Discrete-event simulation: Core theory of discrete-event simulation will be covered, including a review of simulation mechanics, how to incorporate randomness into a simulation, and the systematic analysis of simulation model results. This will be supplemented with practical training in how to build and run simulation models using commercial software. Example applications will be drawn from a variety of sectors, such as manufacturing/production, transportation, healthcare, and other service industries (e.g. banking, retail, customer service).
Total contact hours: 35
Private study hours: 115
Total study hours: 150
Main assessment methods:
VLE test 1: Queuing Theory Exercises: 20%
VLE test 2: 20%
Simulation Modelling Report (up to 2500 words): 60%
Reassessment methods:
Reassessment Instrument: 100% coursework
The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices.
The most up to date reading list for each module can be found on the university's reading list pages.
See the library reading list for this module (Canterbury)
The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
- Recognise the types of business and organisational problems that can be appropriately formulated and analysed using stochastic simulation.
- Demonstrate a conceptual understanding of the basis of queuing theory.
- Build realistic simulation models using industry-standard software and acquire a systematic understanding of the flexibility that simulation based approaches provide managers in terms of dealing with risk and other real-world complexities.
- Demonstrate a comprehensive understanding of the theoretical foundations of stochastic simulation, including random number generation, sampling from discrete and continuous distributions, and statistical analysis of transient/steady-state outputs.
The intended generic learning outcomes.
On successfully completing the module students will be able to:
- Demonstrate originality in model building, problem-solving, and numerical analysis skills to solve complex problems.
- Use advanced computer tools to solve practical problems of direct relevance to business planning.
- Communicate findings to both specialist and non-specialist audiences in a clear, yet rigorous manner.
University of Kent makes every effort to ensure that module information is accurate for the relevant academic session and to provide educational services as described. However, courses, services and other matters may be subject to change. Please read our full disclaimer.
