Postgraduate Course: Advanced Simulation Techniques (CMSE11420)
||College||College of Arts, Humanities and Social Sciences
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Not available to visiting students
|Summary||In this advanced course on simulation, students will learn additional techniques related to both aspects of modelling and analysis of a simulation problem. Knowledge and understanding of all materials covered in the introductory course Simulation Modelling and Analysis (which runs in the first half of Semester 2) is strictly required.
The Advanced Simulation Techniques course is designed to complete the simulation modelling and analysis skill set of our students for them to be even better placed to run simulation projects of an industrial scale and, as such, will involve the use of additional commercial software products for simulation that are not covered by the introductory course.
In this advanced course on simulation, students will learn additional techniques related to both aspects of modelling and analysis of a simulation problem.
From the modelling perspective, students will learn various graphical formalisms for building conceptual models for subsequent implementation in a simulation software. They will also learn simulation methods beyond Discrete Event Simulation, including Agent-Based Simulation, System Dynamics, Hybrid Simulation, etc.
Additional techniques for experimenting with a simulation model will also be presented to students, including (Factorial) Design of Experiments, the Response Surface Methodology, and techniques that integrate/hybridise the use of both simulation and optimisation techniques/heuristics.
During computer labs, students will learn and use commercial simulation packages that are not used in the introductory course Simulation Modelling and Analysis. This will help them to familiarise with additional simulation products they may encounter in their Business Analytics profession when applying simulation.
- Conceptual Modelling for Simulation
- Agent-Based Simulation
- System Dynamics
- Hybrid Simulation
- Statistical Design and Analysis of Experiments
- Full-Factorial Design of Experiments
- Response Surface Methodology
- Optimum-Seeking Packages for Simulation
Student Learning Experience
Weekly lectures and hands-on programming exercises in the chosen commercial software for simulation (e.g.: Anylogic, Simio), which enables students to implement the methodologies covered in class.
Entry Requirements (not applicable to Visiting Students)
|| It is RECOMMENDED that students have passed
Simulation Modelling and Analysis (CMSE11426)
||Other requirements|| For MSc Business Analytics students, or by permission of course organiser. Please contact the course secretary.
Course Delivery Information
|Academic year 2019/20, Not available to visiting students (SS1)
||Block 4 (Sem 2)
|Learning and Teaching activities (Further Info)
Lecture Hours 10,
Seminar/Tutorial Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Individual Essay (60% weighting)
Individual Presentation (40% weighting)
Both assess Learning Outcomes 1 to 4.
||Feedback on formative assessed work will be provided in line the Taught Assessment Regulation turnaround period, or in time to be of use in subsequent assessments within the course, whichever is sooner. Summative marks will be returned on a published timetable, which will be communicated to students during semester. All assessments will be marked according to the University Common Marking Scheme.
|No Exam Information
On completion of this course, the student will be able to:
- Discuss the concepts and methods of simulation analytics, in general, and discrete event simulation, in particular, using the proper terminology;
- Identify and properly state decision problems in different business settings, model them using a simulation framework, verify and validate the model, choose the right solution methodology and methods and solve them using simulation techniques;
- Interpret results/solutions in light of the possible courses of action for a given business problem or situation, formulate managerial guidelines and make recommendations;
- Critically discuss alternative simulation methods for a given problem, and choose the most appropriate ones ahead of implementation.
|Law, A.M. (2014) Simulation Modeling and Analysis (5th edition), McGraw-Hill|
|Graduate Attributes and Skills
Knowledge integration and application
Analytical, critical and creative thinking
Numeracy and Big Data
|Course organiser||Dr Maurizio Tomasella
|Course secretary||Miss Lauren Millson
Tel: (0131 6)51 3013