Computer Science @ UC Davis | Course Descriptions

ECS 156 DISCRETE EVENT SIMULATION (4) I

Lecture: 3 hours

Discussion: 1 hour

Prerequisite: Programming skill at the level of course 60; calculus-based course in probability theory, such as Statistics 120 or 130A or 131A, Mathematics 135A or Engineering Civil and Environmental 114

Grading: Letter; homework (25%), midterm (50%), final (25%)

Catalog Description:
Design of discrete-event simulation software. Random number generators. Event, process and activity-scanning approaches. Data structures and algorithms for event lists. Statistical output analysis. Applications to computer systems and networks; reliable systems; transportation; business management.

Expanded Course Description:

Infrastructure
1. Review of probability theory
2. Random number generators
Discrete-Event Simulation World Views
1. Event-oriented view
2. Process-oriented view
3. Activity-scanning view
Data Structures for Event-List Processing
1. Linear linked lists
2. Indexed lists
3. Heaps
Output Analysis
1. Review of statistical inference concepts
2. Confidence intervals for space-average simulations
3. Confidence intervals for time-average simulations, using the regenerative method D. Variance-reduction techniques
Applications (interspersed with the above topics throughout the course)
1. Introduction to queuing models, and the need for simulation for mathematically-intractable extensions
2. Applications to computer and network systems;inventory models; transportation systems; etc.

Textbook:
Instructor's printed notes

Computer Usage:
Moderately extensive programming, platform-independent but requiring access to open-source programming tools, e.g. the SimPy simulation language.

Engineering Design Statement:
Students will design detailed simulations of complex systems. In addition to correctness of output, design of the simulation will be aimed at reduction of run-time, and at readability and modifiability of the code.

ABET Category Content:
Engineering Science: 2 units
Engineering Design: 2 units

Goals:
Students will:

gain an understanding of how to write simulation software-in time-efficient manner in both uniprocessor and multiprocess platform
learn various discrete-event simulation applications, interspersed with the software concepts, making the concepts concrete
develop within the students experience in the systems modeling process

Student Outcomes:

An ability to apply knowledge of mathematics, science, and engineering
An ability to design and conduct experiments, as well as to analyze and interpret data
An ability to function on multi-disciplinary teams
An ability to identify, formulate, and solve engineering problems
A recognition of the need for, and an ability to engage in life-long learning
A knowledge of contemporary issues
An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice

Instructor: N.S. Matloff

Prepared by: N.S. Matloff (October 2006)

Overlap Statement:
There are no other courses on discrete-event simulation. Some courses, such as Statistics 32, Engineering Applied Science 117ABC, and Engineering Civil and Environmental 146, use simulation but focus on Monte Carlo or continuous-process contexts, rather than on discrete-event simulation.

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