Total Credits: 10
Level: Level 4
Target Students: MSc and Part III undergraduate students in the School of Computer Science. Also available to Part II undergraduate students in the School of Computer Science subject to Part I performance. Also available to students from other Schools with the agreement of the module convenor. Available to JYA/Erasmus students.
|Autumn||Assessed by end of Autumn Semester|
Summary of Content: This module is part of the Modelling and Optimisation theme in the School of Computer Science. Computational Simulation of systems is becoming an increasingly common due to the recent improvements of speed and memory in computer hardware allowing simulation of realistic systems. System simulation can help to understand the processes currently in place and show the consequences of changes to these processes over time. Successful case studies of simulation include manufacturing, financial system, retail and more recently other areas of the service sector.
Three broad simulation paradigms exist: System Dynamics, Agent-based and Discrete Event. This module will explain each of them in detail so that students will be competent in choosing and implementing the right method for their particular problem.
Topics covered include:
• Introduction to simulation • General principles of simulation • Modelling paradigms (flowcharts, state charts, queuing systems, Petri nets) • Simulation paradigms (System Dynamics, Discrete Event Simulation, Agent Based Simulation, hybrid simulation, parallel and distributed simulation) • Generating random numbers • Input modelling (selecting input probability distributions, creating random variates) • Output data analysis (interpretation of output statistics, confidence intervals, data representation) • Experimental design and sensitivity analysis (warm-up period, run length, replications, design of experiments) • Case studies
Method and Frequency of Class:
|Activity||Number Of Weeks||Number of sessions||Duration of a session|
|Lecture||11 weeks||1 per week||2 hours|
|Computing||11 weeks||1 per week||2 hours|
Method of Assessment:
|Coursework 1||100||Simulation case study: 2500 word coursework|
Professor N Krasnogor
to introduce the principles, techniques and applications of computational simulation
to enable the students to appreciate some of the most widely used simulation techniques and to know which one to choose for their applications (system dynamics, agent based, discrete event)
to enable the students to understand and be able to put into practice computer simulations
Knowledge and Understanding
understanding the capabilities, strengths and limitations of simulation methods (A3)
an appreciation of different simulation techniques (A4)
the ability to understand complex ideas and relate them to specific situations (B4)
the ability to implement selected simulation methods for real world applications (C1)
the ability to evaluate simulation techniques and select those appropriate to a given task (C3)
the ability to address real problems and assess the value of their proposed solutions (D1)
the ability to retrieve and analyse information from a variety of sources and produce detailed written reports on the result (D4)
Offering School: Computer Science
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