| Course title | Modelling and Simulation | |
| Course number | COMP 522A (CRN 1488) Fall Term 2002 | |
| 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 | Leacock 324 (check Minerva). | |
| Monday and Wednesday, 14:30 - 16:00 | ||
| Tentative Schedule | ||
| Enrollment Cap | Enrollment is limited to 45 students. | |
| 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 | |
| TA | Jean-Sébastien Bolduc | |
| TA Office hours | Wednesday 16:00 - 18:00 | |
| McConnell Engineering room 202 (Modelling, Simulation and Design Lab) |
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. Through the assignments (building prototype modelling and simulation tools), some experience in structured, object-oriented design and implementation will also be acquired.
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 and serve as case studies for the tools built in the assignments.
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.
Ex cathedra lectures.
Each group of new topics will be implemented in an assignment using the ``executable pseudocode'' scripting language Python.
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 Projects:
One type of assignment consist of either the development of a small model (e.g., of traffic behaviour at an intersection) in a particular formalism without simulation or of the use of an existing modelling and simulation system such as GPSS or PythonDEVS to obtain performance metrics through simulation.
The other type of assignment includes software development. The rationale is that the Modelling and Simulation software tools to be developed are representative for ``complex'' software systems. During the development, most of the typical software engineering problems will be encountered. Obviously, the development of these tools will also provide insight into the (often abstract) Modelling and Simulation concepts.
Structure:
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.
Assignments and projects will be judged on:
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. See section 15 of Student Rights and Responsibilities Handbook for more details.
Background information for the course can be found in:
All the above are available at the Schulich library.