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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 and methodology can do, and their role within the broader context of Business Analytics. Lectures will 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 workshops, students will learn and use a commercial simulation package, and focus on Discrete Event Simulation modelling and analysis of simple examples. Later lectures and classes will introduce other simulation methods, including Agent-Based Simulation, System Dynamics and hybridised approaches where multiple methods are jointly employed within the same modelling effort. In this context, introductory tutorials will be provided of software with such capabilities. All simulation software packages employed in the course are commercial and widely used in industry. The individual project coursework will give students an opportunity to put their learning of simulation modelling and analysis techniques to the test, while working on a realistic/real simulation project.
Course 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 'Discrete Event Simulation', the most widely used of them all. 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 partial shift in 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 Analytics problem.

Content outline:
- Discrete Event Simulation modelling
- Steps in a sound simulation study
- Random Input in Simulation Modelling
- Analysis of Simulation Output for a Single System
- Analysis of Simulation Output: Multiple Systems Comparisons
- Design and Analysis of simulation experiments
- Validation, Verification and Credibility of simulation models and analyses

Student Learning Experience:
Each week's workload will be subdivided in portions. A small amount of short readings will also be provided as either compulsory short readings or additional material for those students who want to deepen their knowledge of certain aspects of the subject. All teaching materials are meant for daily consumption by students, following either a self-imposed pace and timeline or, as strongly recommended, an agenda proposed directly by the course organiser. On completion of the computer workshop material, which is based on simplified examples to foster learning of the technical components of the subject, students will begin to work on their individual coursework project. This will consist of a real/realistic simulation analytics project, either involving an actual client or, alternatively (on a year by year basis), based on a previous real-world project that has been simplified in a way to make it manageable for completion within the time available.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2022/23, Not available to visiting students (SS1) Quota:  60
Course Start Block 4 (Sem 2)
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 10, Seminar/Tutorial Hours 11, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 77 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 100% coursework (individual) - assesses all course Learning Outcomes
Feedback Formative feedback:
Formative feedback to each student's individual learning process will be given during the small group computer teaching lab sessions at the end of each week of teaching. Students will be expected to come to these sessions having worked on the course materials from the same week, so that specific feedback on their progress can be provided by the curse organiser and the teaching assistant. This formative feedback will be central in supporting each student's own work on the individual submission for the final assessment.

Summative feedback:
Summative feedback will be in written form, and will come with the final mark on the coursework project.

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
Robinson, S. (2014) Simulation: The Practice of Model Development and Use (2nd edition), Red Globe Press
Winter Simulation Conference Archive (online and open, available at
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 secretaryMs Emily Davis
Tel: (0131 6)51 7112
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