Proposed Tentative Syllabus:

BS 490

Simulation Modeling

Spring, 1999

Text: Kelton, W. David, Randall P. Sadowski, and Deborah A. Sadowski. Simulation with Arena. Boston: McGraw-Hill, 1998.

Prerequisites: MA 251, Applied Statistics and Computer proficiency

Course objectives:

  1. to understand simulation as a problem-solving methodology
  2. to develop modeling skills using simulation software
  3. to build on previous study of probability and statistics
  4. to experience the challenges of modeling a real-life system

Course elements:

  1. Problems: extensive use of examples 25%
  2. Examinations: midterm and final exams 35%
  3. Project: Team-based modeling of a real-life system. 40%

Course description:

This course is designed to examine the use of simulation to model problems in business and computer systems. These problems are characterized by entities (e.g., jobs, customers, materials, etc.) that enter, move around in, affect other entities in, and exit a system. Decisions regarding the design and operation of such systems are enhanced by the model-produced information. Manufacturing and logistics, customer interaction, computer system performance problems are typical examples of such systems. The modeling effort attempts to represent the significant elements (e.g., input, flows, queues, output) of the problem in a computer model.

The primary software for modeling is Arena, a Microsoft Windows-based application. Arena provides modelers with an object-oriented approach to designing a representation of the problem. Arena also provides an animation option for the dynamic depiction of the modeled system. The course will meet in a computer lab, and software is included with the proposed textbook. Students will develop as a team a project simulating a real-life system. As schedules permit, guest speakers from industry may visit to describe the practice of simulation. A site visit is another alternative.

 

    Proposed Schedule (weeks)

     

  1. Prerequisite Review: Probability and Statistics for Simulation:

  2. Probability basics

  3. Random variables

  4. Sampling

  5. Descriptive measures

  6. Inference: Estimation and hypothesis tests
    7.
  7. Spreadsheet simulation: jumpstart into modeling and random variables and decision making

  8. Project management example
    9.
  9. Simulation concepts:

  10. Modeling systems

  11. Input processing output

  12. Resources
    13.
  13. Queueing concepts

  14. Examples and application

  15. Introduction to Arena

  16. Midterm Exam

  17. Developing basic models: input analysis, resource utilization, animation, output analysis. Random number generation

  18. Developing more complex models: multiple entity types, queue disciplines and behavior,

  19. Complex modeling

  20. Simulation Study Issues:
  21. Verification and validation
  22. Experimental design

    23.
  23. Intermediate Arena modeling:
  24. Visual Basic for Applications (VBA)
  25. Integration with Microsoft Office: Excel and Powerpoint
    26.
  26. Project presentations
  27. Final Exam