Postgraduate Course: Simulation Modelling and Analysis (CMSE11426)
||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 entry-level course on simulation, students will learn what simulation techniques can do and their role within the broader context of Business Analytics.
Lectures will first introduce the student to the main aspects of running a simulation project, and then proceed to discuss selected details such as input and output data analysis.
During computer labs, students will learn and use a commercial simulation package, and focus on Discrete Event Simulation modelling and analysis of simple examples.
This course deals with the various aspects that are related to the planning and running of a simulation project, within the broader context of Prescriptive Analytics and, more generally, Business Analytics. Of all simulation methods available, the course will focus on the most widely used of them, i.e. 'Discrete Event Simulation'. The Basics of Probability and Statistics that are needed to successfully apply simulation are, albeit already covered in Semester 1 courses, briefly reminded to the student in the earlier sessions of this course. The reason is that those basics need to be put in the context of simulation modelling and analysis, something which requires a shift of mindset from the part of the student in the way they think about basic techniques such as those for building confidence intervals. The course then covers all main aspects related to the inclusion of random (stochastic) elements in simulation models, namely: random number generation, random variate generation and input data analysis. Finally, the course discusses techniques for the analysis of output data from simulation runs, including the analysis of a single system and, more importantly, the comparison of multiple system alternative configurations in order to choose the 'best' course of action for a given business problem.
- Discrete Event Simulation modelling
- Steps in a Sound Simulation Study
- Probability and Statistics for Simulation
- Random Input in Simulation Modelling
- Analysis of Simulation Output for a Single System
- Analysis of Simulation Output: Multiple Systems Comparisons
Student Learning Experience
Weekly lectures and hands-on programming exercises in the chosen commercial software for simulation, which enables students to implement the methodologies covered in class.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| For MSc Business Analytics students, or by permission of course organiser. Please contact the course secretary.
Course Delivery Information
|Academic year 2020/21, 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)
||Practical assessment (100% weighting)
The assessment is a computer-based test of the students' skill set in modelling and analysis of Business Analytics problems through simulation, following all learning from the course.
Simulation Modelling and Analysis is very much a methodology-intensive, extremely hands-on course. Assessment shall therefore involve the demonstration, by the student, of the related computing and analytical skills.
The assessment will assess all the learning outcomes.
Students will be given the detailed description of a realistic business related situation to be studied through simulation modelling analysis, and afterwards given a small number of questions, in relation to the problem situation under scrutiny, for them to answer. Each question in the individual coursework will focus on one specific aspect of this simulation study, e.g.: conceptual modelling of the situation; building a small simulation model to quickly analyse one or more aspects of the related problem; analysing certain ouptut from the simulation of the problem situation, as provided to the student and without the need to run further simulation replications; etc.
||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, and 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.
|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||Ms Emily Davis
Tel: (0131 6)51 7112