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DRPS : Course Catalogue : Business School : Common Courses (Management School)

Postgraduate Course: Simulation Modelling and Analysis (CMSE11426)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryIn 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.
Course description Academic Description
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.

Outline Content
- 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 (currently: Rockwell Arena), which enables students to implement the methodologies covered in class.

Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations 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) Quota:  47
Course Start Block 3 (Sem 2)
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 10, Seminar/Tutorial Hours 10, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 78 )
Assessment (Further Info) Written Exam 0 %, Coursework 0 %, Practical Exam 100 %
Additional Information (Assessment) Individual Practical Exam (100% weighting)

The Practical Exam 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 skills. The assessment will assess just that, and, more specifically, all the learning outcomes discussed above. Exercise questions in the Practical Exam will include, for instance (this is by no means an exhaustive list):
- Analysis of simulation output provided;
- Building of a small simulation model to quickly analyse a problem question;
- Running a simulation model provided in order to understand some aspect of the related system.
Feedback 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
Learning Outcomes
On completion of this course, the student will be able to:
  1. Discuss the concepts and methods of simulation analytics, in general, and discrete event simulation, in particular, using the proper terminology;
  2. 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;
  3. Interpret results/solutions in light of the possible courses of action for a given business problem or situation, formulate managerial guidelines and make recommendations.
Reading List
Law, A.M. (2014) Simulation Modeling and Analysis (5th edition), McGraw-Hill
Additional Information
Graduate Attributes and Skills Problem Solving
Knowledge integration and application
Analytical, critical and creative thinking
Numeracy and Big Data
Written communication
KeywordsNot entered
Course organiserDr Maurizio Tomasella
Course secretaryMiss Lauren Millson
Tel: (0131 6)51 3013
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