Module Code: H9MSO
Long Title Modelling, Simulation & Optimization
Title Modelling, Simulation & Optimization
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 10
Module Coordinator: Shauni Hegarty
Module Author: Margarete Silva
Departments: School of Computing
Specifications of the qualifications and experience required of staff  
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Categorize different types of simulation, modelling, and optimisation technologies
LO2 Implement and test a conceptual model using a simulation tool
LO3 Critically analyse output data produced by a model and test the validity of the model
LO4 Perform optimisation according to chosen criteria
LO5 Comprehend, reflect on and combine some of the most commonly used modelling and simulation methods and optimisation heuristics
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  

Module Content & Assessment

Indicative Content
Linear Programming
Linear Programming, application in production planning
Discrete optimisation techniques
Integer programming, constraint programming, application in scheduling
General optimisation, Multi-objective optimisation
Test Problems, Classical methods, advanced Methods, Pareto optimality
Local search. Simulated annealing. Tabu search. Variable neighbourhood search, applications.
Evolutionary algorithms
Genetic algorithms Swarm intelligence Memetic algorithms swarm intelligence
Hybrid metaheuristics and Applications
Combining metaheuristics with mathematical programming, constraint programming, application in machine learning and datamining, applications in Decision Support Systems
Introduction to Simulation
Concept of system, model and simulation, simulation methodologies, components of discrete event simulation, verification and validation of simulation systems
Queueing system
Characteristic of a queueing system, Simulation of single server queueing system
Output data analysis for single server system
Probability distribution functions, Estimation of statistical parameters, Applications of Single Server Systems
Integrated Simulation Studies
Statistical models in simulation, Object-Oriented Simulation, Building a larger simulation system intelligence
Continuous Simulation
Use of Differential Equations, Runge-Kutta Integration, Predator-Prey Systems, Infectious Disease Modelling
Agent-Based Simulation
Verification, validation and credibility of simulation models, simulation of manufacturing, crowd simulation
Assessment Breakdown%
End of Module Assessment40.00%


Full Time

Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: Yes
Assessment Description:
Formative assessment will be provided on the in-class individual or group activities. Feedback will be provided in written or oral format, or on-line through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning.
Assessment Type: Project % of total: 60
Assessment Date: n/a Outcome addressed: 2,3,4
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: 1,5
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 Every Week 24.00
Tutorial Other hours (Practical/Tutorial) 24 Every Week 24.00
Independent Learning Independent learning (hours) 202 Every Week 202.00
Total Weekly Contact Hours 48.00

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.
  • Banks , J.. (2010), , Discrete-Event System Simulation, Pearson Education.
  • Simon, D.. (2013), Evolutionary Optimization Algorithms, Wiley.
  • Alan Sultan. (2011), Linear Programming, CreateSpace, p.646, [ISBN: 978-1463543679].
  • Mandal, J.K & Mukhopadhyay, S. & Dutta, P.. (2018), Multi-Objective Optimization: Evolutionary to Hybrid Framework, Springer Singapore.
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: