Modelling and Simulation

Modelling and Simulation
Fall Term 2006

General Information

Course title Modelling and Simulation
Course number COMP 522A (CRN 921) Fall Term 2006
Prerequisites CS 308-251 (data structures and algorithms),
CS 308-302 (programming languages and paradigms),
CS 308-350 (numerical computing).
Some experience with object-oriented design and programming.
If you do not have the pre-requisites for the course, the course will be
deleted from your record by the Faculty of Science.
This is a "project" course which means you will learn to build and use
full modelling and simulation tools in the numerous assignments.
You are strongly advised to not take more than one other "project" course.
Course venue Trottier 1080 (check Minerva).
Monday, Wednesday, and Friday 8:00 - 9:30
Tentative Schedule
Instructor Prof. Hans Vangheluwe
McConnell Engineering room 328
tel.: +1 (514) 398 44 46
e-mail: hv@cs.mcgill.ca
Office hours Monday 16:00 - 18:00 in McConnell 328 (or by appointment at other times)
TA Ximeng (Simon) Sun
e-mail: xsun16@cs.mcgill.ca
TA Office hours Wednesday 13:00 - 14:00 (or by appointment at other times)
McConnell Engineering room 202 (Modelling, Simulation and Design Lab)

Course Concepts

Course Goals

Objectives

The course aims to teach the generic (i.e., tool and application domain independent) concepts of modelling and simulation. By the end of this course, you should have a deep understanding of the concepts of modelling and simulation of dynamic systems using a variety of formalisms. You should be able to build modelling and simulation systems. This will give you ample background to understand and use existing modelling and simulation systems. The course presents general modelling and simulation principles by applying them to concrete problems.

Prototype modelling tools and simulators are constructed based on the theory. This is preferred to using commercial simulation tools as it leads to much better insight.

Applications (software process modelling and simulation, reactive systems design such as complex graphical user interfaces, population dynamics analysis, traffic analysis, supermarket queueing, etc. ) are used to illustrate the different modelling formalisms.

Rationale and Content

In the course, a bird's eye view of the state-of-the-art in modelling and simulation is presented. Hereby, the close relationship between modelling and simulation on the one hand and the analysis and design of complex (software and hardware) systems is highlighted. A formal specification of modelling and simulation formalisms and processes reveals the need for a host of computer science techniques such as graph algorithms, compilers, computer algebra, software engineering, and graphical user interfaces. By means of these techniques, modelling and simulation tools are developed.

The modelling and simulation formalisms and tools are highly useful for the analysis, design, and implementation of complex, often embedded software systems, interacting with the physical world.

The complexity of current and future systems is not only due to a large number of components (tackled by hierarchical decomposition), but is also caused by the increasing diversity of components and problem aspects. A photocopier for example combines software, analog electronic, digital electronic, electrostatic, thermodynamic, hydraulic, ... aspects and components. To easily express the structure and behaviour of such systems, multiple formalisms must be used. Such models are a basis for documentation, analysis, formal proof, simulation what-if analysis, optimization and (embedded) application code generation.

The course presents a holistic view of the modelling and simulation enterprise. Rather than focusing on particular applications, or on specific tools, it starts from a general methodology which stresses the generic, application-independent aspects of modelling formalisms and their implementation. The main aim of the course is to provide the theoretical background, methods, techniques and tools for complex problem solving, with emphasis on the software aspects.

The formalisms covered range from Causal Block Diagrams, Differential Algebraic Equations, Forrester System Dynamics, to Finite State Automata, Statecharts, Petri Nets, DEVS, and the different Discrete Event World Views. More importantly, the relationships between these, as well as their relative merits and disadvantages, are investigated. For each formalism, the design and implementation of a solver or simulation kernel is presented.

From the practitioner's point of view, the course describes different modelling formalisms, existing languages and to a lesser extent, tools. From the computer scientist's point of view, the course describes the techniques and standards employed in the construction of modelling environments and simulators.

Each of the topics is introduced by means of an example, followed by a theoretical presentation, followed by a more in-depth example.

Method of Instruction

Ex cathedra lectures.

Each group of new topics will be implemented in an assignment using the "executable pseudocode" scripting language Python.

Course Materials

There are no required texts.

Course material can be downloaded from the course homepage, and a selection of book chapters will need to be copied. For a couple of lectures, you will need to take notes in class.

Assignments and Evaluations

Assignments and Projects:

Structure:

Grading

Grades will be distributed over assignments, project and exam:

Students with marks of D, F or J will have the option of doing Additional work to improve assignment/project grades. Note how this extra work is treated as a supplemental exam and requires obtaining faculty permission !

Assignments and projects will be judged on:

Original Work

You are encouraged to help each other formulate the ideas behind assignment problems, but each student is required to submit his or her own original work. Handing in work that is not your own, original work as if it is your own is plagiarism.

McGill University values academic integrity. Therefore all students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the code of student conduct and disciplinary procedures (see www.mcgill.ca/integrity for more information).

References

Background information for the course can be found in:

All the above are available at the Schulich library.