EGM530
Stochastic Models in Engineering Management

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Course Description: A review of engineering analytical methods and their application in strategic decision environments. Required case studies will entail techniques such as Monte Carlo simulation, risk assessment, and failure modeling as the suitability and application of several engineering analytical approaches to operational analysis of business/industry decision processes. Engineering Project Managers and designers usually have a goal of highest performance at lowest cost. Modeling and simulation of system design trade off is good preparation for design and engineering decisions in real world jobs. In this class we study systems modeling and simulation. We need a proper knowledge of both the techniques of simulation modeling and the simulated systems themselves.

Text: A.M. Law, Simulation Modeling and Analysis fourth edition , McGraw Hill, 2007.

Lecture: 3 hours lecture

Course Objectives: Upon completion of this course a student should be able to:

  • Understand how simulation modeling can be used in solving various real world problems and improving the performance of existing systems.
  • Identify which problems are best suited to simulation modeling.
  • Design and implement a system model using a simulation language, as well as select the appropriate analysis method.
  • Understand the randomness in a system and how to model it.
  • Demonstrate their ability to solve real-life problems by using simulation.
  • Realize the importance of using input and output analysis of simulation models.
  • Graduate students should further be able to: Demonstrate a broader understanding of the theoretical aspects and basics of simulation modeling, by developing two large simulation projects.
Lecture Topics:
  • Basic Simulation Modeling
  • Modeling Complex System
  • Simulation Languages for Modeling
  • Valid and Credible Simulation Models
  • Data Collection and Analysis
  • Random-Numbers and Random-Variate Generation
  • Output Data Analysis
  • Simulation (Model Experimentation) and Optimization

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