Prerequisites

An undergraduate course on probability and/or statistics or graduate standing

Student Learning Objectives

Upon successful completion of this course, students shall be able to:

  • Identify engineering problems related to the efficient production of goods and services.
  • Measure, evaluate and improve production processes and systems to produce goods and services.
  • Design efficient production processes and systems to produce goods and services.
  • Communicate effectively in a professional role with specific capability to write technical reports and presents results effectively.

Course Description

This course introduces discrete event simulation (through Arena) and how it is applied to dynamic systems. Discrete event simulation concepts such as entities, resources, and event chains are introduced. Systems will be simulated considering time (such as work schedules, machine/human performance), space (such as process layout) and resource (such as manpower, equipment) characteristics of process. In doing that, the course covers a variety of Arena features and building blocks. The course will also introduce time studies, and distribution fitting.

Past Syllabus

Sample Lectures

The following two lectures describe the Buffon Needle Experiment to explain the primary concepts of discrete event simulation.

Buffon Needle Experiment Part 1

Buffon Needle Experiment and Modern Simulation 

Students are assumed to have never used any simulation software prior to taking this course. In four projects, students use Rockwell's Arena software to build complex systems.  Through analyzing the output, students identify inefficiencies and recommend improvements.  Students complete these projects by remotely logging in to the U's CADE computer lab, where they build the simulation models.   The following lecture is the second lecture in a set of lectures introducing students to the Arena software.

Arena Introduction part 2

Any competent analysis of discrete event simulation output requires statistics.  A substantial amount of lectures are spent on assuring that students understand how to statistically analyze the output from discrete event simulation.  The statistical discussion is so indepth that this class or SIME 5000-6000 is encouraged to be taken early if a person is concerned about their mastery of their statistical knowledge.