Module Code: H8SMSOA
Long Title Systems Modelling, Simulation & Optimization for Analytics
Title Systems Modelling, Simulation & Optimization for Analytics
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 10
Module Coordinator: Ade Fajemisin
Module Author: Ade Fajemisin
Departments: School of Computing
Specifications of the qualifications and experience required of staff


Master’s degree or PhD in a computing or cognate discipline. May have industry experience also.
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Categorize different types of simulation modelling technologies
LO2 Implement a conceptual model using a simulation tool
LO3 Generate and test random number variates and apply them to develop simulation models
LO4 Derive correct and efficient sampling algorithms for given probability distributions
LO5 Analyse output data produced by a model and test the validity of the model
LO6 Perform optimisation according to chosen criteria
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

Learners should have attained the knowledge, skills and competence gained from stage 3 of the BSc (Hons) in Data Science
 

Module Content & Assessment

Indicative Content
Introduction
Concept of system, model and simulation, components of discrete event simulation
Simulation methodologies
Continuous, discrete, Monte Carlo, agent-based, system dynamics, games and virtual worlds
Mathematical modelling languages for the description of distributed systems
Petri nets, UML activity diagrams, Event-driven process chains, Markov chains, etc.
Statistical models
Statistical models in simulation, Probability distribution functions, Estimation of statistical parameters.
Random numbers
Generation of random number and random number variables, testing of random numbers.
Queueing system
Characteristic of a queueing system, Simulation of single server queueing system
Input modelling
Estimation of parameters, Fit tests of distributions
Output data analysis for single system
Statistical analysis for terminating and non-terminating simulations, comparing alternative system configurations
Testing
Verification, validation and credibility of simulation models, simulation of manufacturing, material handling systems, traffic
Simulation-based optimization
Statistical ranking and selection methods, response surface methodology, heuristic methods, derivative-free optimization methods
Advanced optimization techniques
Metaheuristics (genetic algorithms, simulated annealing, tabu search, etc.)
Case studies
Production scheduling, planning, gaming, traffic, healthcare. Ethics aspects
Assessment Breakdown%
Coursework60.00%
End of Module Assessment40.00%

Assessments

Full Time

Coursework
Assessment Type: Continuous Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4,5,6
Non-Marked: Yes
Assessment Description:
Ongoing independent and group problem solving activities and feedback.
Assessment Type: Project % of total: 60
Assessment Date: n/a Outcome addressed: 2,3,4,5,6
Non-Marked: No
Assessment Description:
Long-form project which the student produces over the course of the entire semester. Student is required to model and simulate a process (production scheduling, planning, gaming, traffic, operating theatre) using a simulation tool using an open source simulation tool
End of Module Assessment
Assessment Type: Terminal Exam % of total: 40
Assessment Date: End-of-Semester Outcome addressed: 1,2,3,4,5,6
Non-Marked: No
Assessment Description:
Terminal assessment exam taken over 2 hours with four questions of which the student must answer three to address the students' understanding of the underlying theories and concepts
No Workplace Assessment
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
Reassessment Description
The repeat strategy for this module is an examination. All learning outcomes will be assessed in the repeat exam.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom & Demonstrations (hours) 24 Per Semester 2.00
Tutorial Other hours (Practical/Tutorial) 24 Per Semester 2.00
Independent Learning Independent learning (hours) 202 Per Semester 16.83
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • Borshchev, A.. (2014), , The Big Book of Simulation Modeling: Multimethod Modeling with Anylogic 6, AnyLogic North America.
  • Choi, B.K. & Kang, D.. (2013), , Modeling and Simulation of Discrete Event Systems, Wiley Press.
  • Altiok, T. & Melamed, B.. (2007), Simulation Modeling and Analysis with Arena, Elsevier Academic Press.
  • Banks , J.. (2010), , Discrete-Event System Simulation, Pearson Education.
Supplementary Book Resources
  • Kelton, W.D., Sadowski, R., and Zupick, N.. (2014), , Simulation with Arena, McGraw-Hill.
  • Evans, J.R. & Olson, D.L.. (2001), , Introduction to Simulation and Risk Analysis, Prentice Hall.
  • Zeigler, B.P., Praehofer, H. & Kim, T.G.. (2000), , Theory of Modeling and Simulation: Integrating Discrete Event, and Continuous Complex Dynamic Systems, Elsevier Academic Press.
This module does not have any article/paper resources
This module does not have any other resources
Discussion Note: