CS 415/ CS 572 Modeling and Simulation of Systems (Autumn 2023)

Elective course

Lecture Timings

• Tuesdays 11am-12noon

• Wednesdays 10am-11am

• Thursdays 8am-9am

• Link to course calendar

• Venue: LT1 (main building)

About This Course

Simulation plays a critical role in the design, analysis and optimization of complex systems in most engineering disciplines. This course focuses on the simulation of Discrete-event systems (that is, systems in which the state is assumed to change at discrete time-instants only, as opposed to a system where the state evolves continuously in time). Computer networks, manufacturing systems, clocked digital circuits and inventory systems are some examples where discrete-event simulation is widely used.

The objectives of this course are to help the student build a strong intuition of how discrete-event simulation works, and to develop the ability to model and simulate a given system effectively and interpret the results correctly. Programming-based assignments will form a significant component of the course. The course will make use of Python's SimPy library.

Topics:

• Introduction to models and simulation, types of simulation.

• Introduction to Python's SimPy library using examples.

• Approaches to discrete-event simulation.

• How SimPy works

• Review of basic probability theory and Markov chains.

• Generation of Random Numbers and Random Variates

• Simulation-based estimation of performance measures

• Queueing models

• Input modeling (fitting distributions to observed data)

• Examples and use-cases (computer networks, manufacturing systems etc).

• [Optional] Parallel and distributed simulation.

Prerequisites

Basic probability theory, Familiarity with Python

References

• Discrete-Event System Simulation, 5th Edition, By: Jerry Banks, John S. Carson II, Barry L. Nelson, David M. Nicol

• Probability, Markov Chains, Queues, and Simulation, by William J. Stewart

• SimPy Documentation

• Parallel and Distributed Simulation Systems, by Richard Fujimoto

Tentative Evaluation Scheme (3 credits)

• Programming-based Assignments: 40%

• Mid-semester Exam: 20%

• End-semester Exam: 30%

• Team Project: 10%