| Module Code: |
H9MSO |
|
Long Title
|
Modelling, Simulation & Optimization
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|
Title
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Modelling, Simulation & Optimization
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| Module Level: |
LEVEL 9 |
| EQF Level: |
7 |
| EHEA Level: |
Second Cycle |
| Module Coordinator: |
Ade Fajemisin |
| Module Author: |
Margarete Silva |
| Departments: |
School of Computing
|
| Specifications of the qualifications and experience required of staff |
This module requires a lecturer holding a Master’s degree or higher, in a discipline with a significant statistics component. e.g. Statistics, Mathematics, Economics
|
| 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 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, apply and develop new (hybrid) methodologies of the most commonly used heuristics (Greedy, Simulated Annealing, Tabu Search, Evolutionary algorithms, Ant Colony optimization) |
| 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
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| No Co-requisite modules listed |
| Entry requirements |
A level 8 degree or its equivalent in a cognate discipline
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Module Content & Assessment
| Indicative Content |
|
Introduction
Concept of system, model and simulation, components of discrete event simulation
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Simulation methodologies
Continuous, discrete, Monte Carlo, agent-based, system dynamics, games and virtual worlds
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Statistical models
Statistical models in simulation, Probability distribution functions, Estimation of statistical parameters.
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Queueing system
Characteristic of a queueing system, Simulation of single server queueing system
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Output data analysis for single system
Statistical analysis for terminating and non-terminating simulations, comparing alternative system configurations
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Testing
Verification, validation and credibility of simulation models, simulation of manufacturing, material handling systems, traffic
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Discrete optimisation techniques
Integer programming, Linear Programming, constraint programming
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Multi-objective optimisation
Classical methods, advanced Methods, Pareto optimality
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Metaheuristics
Fitness landscapes. Local search. Simulated annealing. Tabu search. Variable neighbourhood search
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Evolutionary algorithms
Genetic algorithms. Swarm intelligence. Memetic algorithms swarm intelligence
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Hybrid metaheuristics
Combining metaheuristics with mathematical programming, constraint programming, machine learning and data mining
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Applications
Analytical customer relationship management, Clinical decision support systems, Direct marketing, Fraud detection
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| Assessment Breakdown | % |
|---|
| Coursework | 60.00% |
| End of Module Assessment | 40.00% |
AssessmentsFull Time
| Coursework |
| 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: |
2,3,4,5 |
| 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 |
|
| Assessment Type: |
Easter Examination |
% of total: |
40 |
| Assessment Date: |
n/a |
Outcome addressed: |
2,3,4,5 |
| 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 End of Module 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.
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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 |
|---|
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Borshchev, A.. (2014), , The Big Book of Simulation Modeling: Multimethod Modeling with Anylogic 6, AnyLogic North America.
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Choi, B.K. & Kang, D.. (2013), , Modeling and Simulation of Discrete Event Systems, Wiley Press.
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Banks , J.. (2010), , Discrete-Event System Simulation, Pearson Education.
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Simon, D.. (2013), Evolutionary Optimization Algorithms, Wiley.
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Bertsekas, D. & Tsitsiklis, J.N.. (1997), , Introduction to Linear Optimization, Athena Scientific.
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Mandal, J.K & Mukhopadhyay, S. & Dutta, P.. (2018), Multi-Objective Optimization: Evolutionary to Hybrid Framework, Springer Singapore.
| | Supplementary Book Resources |
|---|
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Kelton, W.D., Sadowski, R., and Zupick, N.. (2014), , Simulation with Arena, McGraw-Hill.
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Evans, J.R. & Olson, D.L.. (2001), , Introduction to Simulation and Risk Analysis, Prentice Hall.
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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 |
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