ACM40270 Simulation Modelling and Analysis

Academic Year 2016/2017

Probabilistic models and their analysis - both experimentally (by simulation) and theoretically (using ideas from probability theory)- have become so important that they are used as a major tool in virtually every scientific field. The course will cover the basics of modeling, simulation and analysis, including developing the theoretical background needed in probability theory and stochastic processes. The course includes (i) a focus on discrete-event simulation, which is used to model queuing behavior in systems such as manufacturing processes, service sector business processes, call center operations, and hospital emergency rooms, (ii) an introduction to the principles and methods of Monte Carlo simulation modeling and (iii) stochastic process approaches including computational simulation.

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Curricular information is subject to change

Learning Outcomes:

Understand and implement modelling, simulation and analysis for(1) Discrete event simulations.(2) Monte Carlo simulations.(3) Stochastic process approaches.

Student Effort Hours: 
Student Effort Type Hours
Lectures

36
Autonomous Student Learning

114
Total

150
 
Requirements, Exclusions and Recommendations

Not applicable to this module.



 
Description % of Final Grade Timing
Examination: End of Semester Examination

70

 2 hour End of Trimester Exam
Continuous Assessment: varies over semester

30

 Varies over the Trimester

Compensation

This module is not passable by compensation

Resit Opportunities

End of Semester Exam

Remediation

If you fail this module you may repeat, resit or substitute where permissible