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

Program Outcomes:

The ability to work independently
The ability to work on a team
The ability to apply knowledge of basic science, mathematics and engineering principles to solve computing and information processing problems
The ability to write correct and good programs
The ability to understand the relationship between hardware and software
The ability to understand the tradeoffs in the design of hardware systems, software systems, processes and components
The ability to construct appropriate abstractions to manage complexity and to think creatively about new problems
The ability to use experimental methods on software systems by gathering data to improve the systems
The ability to acquire the foundations to do well in graduate school
The ability to acquire the foundations to be a life-long learner

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.

3/07