Modeling and Optimization in Systems Engineering

ENSC 460-4

 

Course Description

This course provides an introduction to mathematical models and optimization methods in systems engineering. Topics include algebraic and geometric representation of optimization models, gradient methods for unconstrained optimization, computational methods for nonlinear and linear constrained optimization models, stochastic system models based on Markov chains, models and computational techniques for discrete optimization, and multi-agent models based on holonic systems. Examples are selected from robotics, manufacturing, telecommunications, transportation, and other systems engineering disciplines. Laboratory exercises provide familiarization with the use of optimization software.

Course Objectives

This course is an upper division elective in the Systems Option of Engineering Science. It is intended to provide students with a knowledge of methods for formulating and solving optimization models in systems engineering. It provides background for the design and analysis of complex systems studied in ENSC 320, ENSC 383, ENSC 429, ENSC 483, ENSC 488, and related engineering science courses.

Prerequisites

Required: MATH 232, MATH 251, and STAT 270

References

  1. F. Hillier and G. Lieberman, Introduction to Operations Research, McGraw-Hill, 1990.
  2. R. Rardin, Optimization in Operations Research , Prentice Hall, 1998.
  3. A. Ravindran, D. Phillips, and J. Solberg, Operations Research, Principles and Practice , John Wiley & Sons, 1987.
  4. A. Belegundu and T. R. Chandrapatla, Optimization Concepts and Applications in Engineering, PRence Hall, 1999.
  5. D. G. Luenberger, Introduction to Linear and Nonlinear Programming, Addison Wesley, 1984.
  6. G. V. Reklaitis, A. Ravindran, and K. M. Ragsdell, Engineering Optimization: Methods and Applications , John Wiley & Sons, 1983.
  7. T. Coleman, M. Brace, and A. Grace, User's Guide for the Optimization Toolbox , Math Works, Inc., 1999.

Course Webpage

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