Optimization, Modeling, and Simulation Training

Commitment 2 Days, 7-8 hours a day.
Language English
User Ratings Average User Rating 4.8 See what learners said
Delivery Options Instructor-Led Onsite, Online, and Classroom Live


Optimization, Modeling, and Simulation Training is an introduction to two closely related areas: (1) stochastic search methods for system optimization and (2) the analysis and construction of Monte Carlo simulations. A few of the many areas where stochastic optimization and simulation-based approaches have emerged as indispensable include decision aiding, prototype development for large-scale control systems, performance analysis of communication networks, control and scheduling of complex manufacturing processes, and computer-based personnel training.

The Optimization, Modeling, and Simulation course focuses on core issues in algorithm design and mathematical modeling, together with implications for practical implementation. The course does not dwell on theoretical details related to the methods; attendees are directed to the appropriate literature for such details. Attendees should have a solid working knowledge of probability and statistics at the beginning graduate level and knowledge of multivariate calculus, basic matrix analysis, and linear algebra. To aid understanding, the course will include a brief review of the prerequisite mathematical material.

  • 2 days of Optimization, Modeling, and Simulation Training with an expert instructor
  • Optimization, Modeling, and Simulation Electronic Course Guide
  • Certificate of Completion
  • 100% Satisfaction Guarantee



Upon completing this Optimization, Modeling, and Simulation Training course, learners will be able to meet these objectives:

  • Popular methods for stochastic optimization.
  • To recognize when stochastic optimization techniques are necessary or beneficial.
  • Advantages and disadvantages of popular methods for system optimization.
  • Essential theoretical principles and assumptions underlying optimization and Monte Carlo simulation and the implications for practical implementation.
  • Basics of mathematical modeling and the link to Monte Carlo simulation.
  • State-of-the-art methods for using Monte Carlo simulations to improve real system performance.
  • We can adapt this Optimization, Modeling, and Simulation Training course to your group’s background and work requirements at little to no added cost.
  • If you are familiar with some aspects of this Optimization, Modeling, and Simulation course, we can omit or shorten their discussion.
  • We can adjust the emphasis placed on the various topics or build the Optimization, Modeling, and Simulation course around the mix of technologies of interest to you (including technologies other than those included in this outline).
  • If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policy-maker), and present the Optimization, Modeling, and Simulation Training course in a manner understandable to lay audiences.

The target audience for this Optimization, Modeling, and Simulation Training course:

  • All

The knowledge and skills that a learner must have before attending this Optimization, Modeling, and Simulation Training course are:

  • N/A


  1. Brief Mathematical Review. Relevant multivariate analysis, matrix algebra, probability, and statistics.
  2. Background on Search and Optimization. Basic issues and definitions. Stochastic vs. deterministic methods. No free lunch theorems for optimization. Summary of classical methods of optimization and their limitations.
  3. Direct Search Techniques. Introduction to direct random search. Monte Carlo methods. Nonlinear simplex (Nelder-Mead) algorithms.
  4. Least-Squares-Type Methods. Recursive methods for linear systems. Recursive least squares (RLS). Least mean squares (LMS). Connection to Kalman filtering. Optimization, Modeling, and Simulation Training
  5. Stochastic Approximation for Linear and Nonlinear Systems. Root-finding and gradient-based stochastic approximation (Robbins-Monro). Gradient-free stochastic approximation: finite-difference (FDSA) and simultaneous perturbation (SPSA) methods.
  6. Search Methods Motivated by Physical Processes. Simulated annealing and related methods. Evolutionary computation and genetic algorithms.
  7. Discrete stochastic optimization. Statistical methods (e.g., ranking and selection, multiple comparisons), general random search methods, and discrete simultaneous perturbation SA (DSPSA).
  8. Model Building. Issues particular to Monte Carlo simulation models. Bias-variance tradeoff. Selecting the “best” model via cross-validation. Fisher information matrix as a summary measure.
  9. Simulation-Based Optimization. Use of Monte Carlo simulations to improve performance of real-world system performance. Gradient-based methods (infinitesimal perturbation analysis and likelihood ratio) and non-gradient-based methods (FDSA, SPSA, etc.). Common random numbers.
  10. Markov Chain Monte Carlo. Monte Carlo methods for difficult calculations; Metropolis-Hastings and Gibbs sampling. Applications to numerical integration and statistical estimation.
  11. Input Selection and Experimental Design. Classical vs. optimal design. A practical criterion for optimal design (D-optimality). Input selection in linear and nonlinear models.
Optimization, Modeling, and Simulation TrainingOptimization, Modeling, and Simulation Training Course Recap, Q/A, and Evaluations


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